58
Global Earth Observation Benefit Assessment: Now, Next, and Emerging GEO-BENE global database for bio-physical modeling v. 1.0 (Concepts, methodologies and data) Rastislav Skalský (1 , Zuzana Tarasovičová (1 , Juraj Balkovič (1 Erwin Schmid (2 , Michael Fuchs (3 , Elena Moltchanova (4 , Georg Kindermann (5 & Peter Scholtz (1 (1 Soil Science and Conservation Research institute, Bratislava, Slovakia (2 University of Natural Resources and Applied Life Sciences, Vienna, Austria (3 Federal Institute for Geosciences and Natural Resources (BGR), Hannover, Germany (4 National Public Health Institute, Helsinki, Finland (5 International Institute for Applied System Analysis, Laxenburg, Austria Summary: Digital database presented here was compiled as a purposeful dataset which is to support GEO-BENE project activities targeted towards societal benefit assessment of improved information coming from data-model fusion approach applied in agriculture and ecosystem modeling. Version 1.0 of the global database is the first approximation to bio-physical model EPIC and agricultural and forestry sector optimization model FASOM requirements and a potential of available global observation data and data coming from other sources addressing climate, topography, soil, and crop management. Several hierarchically organized spatial reference objects were defined to geographically display the global database data. The global scale 5‟ spatial resolution grid covering land surface was created as primary spatial reference for geographical representation of all other spatial objects. Homogenous response units, 30‟ spatial resolution grid and country-level administrative units serve the secondary spatial reference as well as the basis for delineation of elemental spatial units (tertiary reference) for ultimate spatial geographical representation and organization of data in the global database. Global scale and publically available data sources used for global database compilation were described and classified into four groups (global observation data, digital maps, statistical and census data, and results of complex modeling) for the better understanding of the present situation in sources of the data available for global scale agro-ecosystem modeling. Data treatment and interpretation methodologies and the way of data harmonization are described so that solid metadata basis is available for the data publication and exchange. Global database comprises four thematic datasets addressing all global modeling aspects being under consideration: (i) land cover/land use statistics dataset, (ii) topography and soil data dataset, (iii) cropland management dataset, and (iv) climate dataset. Self-standing (v) spatial reference dataset provides a tool for geographical representing and visualization of the data. Data structure of all the datasets tables and attributes is also identified and described in the technical report.

Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

  • Upload
    others

  • View
    2

  • Download
    0

Embed Size (px)

Citation preview

Page 1: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

Global Earth Observation – Benefit Assessment: Now,

Next, and Emerging

GEO-BENE global database for bio-physical modeling v. 1.0

(Concepts, methodologies and data)

Rastislav Skalský(1

, Zuzana Tarasovičová(1

, Juraj Balkovič(1

Erwin Schmid(2

, Michael Fuchs(3

, Elena

Moltchanova(4

, Georg Kindermann(5

& Peter Scholtz(1

(1

Soil Science and Conservation Research institute, Bratislava, Slovakia (2

University of Natural Resources and Applied Life Sciences, Vienna, Austria (3

Federal Institute for Geosciences and Natural Resources (BGR), Hannover, Germany (4

National Public Health Institute, Helsinki, Finland (5

International Institute for Applied System Analysis, Laxenburg, Austria

Summary:

Digital database presented here was compiled as a purposeful dataset which is to support GEO-BENE

project activities targeted towards societal benefit assessment of improved information coming from data-model

fusion approach applied in agriculture and ecosystem modeling. Version 1.0 of the global database is the first

approximation to bio-physical model EPIC and agricultural and forestry sector optimization model FASOM

requirements and a potential of available global observation data and data coming from other sources addressing

climate, topography, soil, and crop management. Several hierarchically organized spatial reference objects were

defined to geographically display the global database data. The global scale 5‟ spatial resolution grid covering

land surface was created as primary spatial reference for geographical representation of all other spatial objects.

Homogenous response units, 30‟ spatial resolution grid and country-level administrative units serve the

secondary spatial reference as well as the basis for delineation of elemental spatial units (tertiary reference) for

ultimate spatial geographical representation and organization of data in the global database. Global scale and

publically available data sources used for global database compilation were described and classified into four

groups (global observation data, digital maps, statistical and census data, and results of complex modeling) for

the better understanding of the present situation in sources of the data available for global scale agro-ecosystem

modeling. Data treatment and interpretation methodologies and the way of data harmonization are described so

that solid metadata basis is available for the data publication and exchange. Global database comprises four

thematic datasets addressing all global modeling aspects being under consideration: (i) land cover/land use

statistics dataset, (ii) topography and soil data dataset, (iii) cropland management dataset, and (iv) climate

dataset. Self-standing (v) spatial reference dataset provides a tool for geographical representing and visualization

of the data. Data structure of all the datasets – tables and attributes – is also identified and described in the

technical report.

Page 2: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

2

Table of content 1. Introduction ......................................................................................................................................................... 3 2. Primary data identification – inputs for the global database ............................................................................... 5

2.1. Global earth observations based digital data ................................................................................................ 5 2.1.1. Digital elevation data (SRTM, GTOPO30) .......................................................................................... 5 2.1.2. Land cover data (GLC2000) ................................................................................................................. 6 2.1.3. Weather data (ECWMF) ....................................................................................................................... 6

2.2. Digital thematic maps .................................................................................................................................. 7 2.2.1. Administrative regions (GAUL) ........................................................................................................... 7 2.2.2. Soil data (DSMW) ................................................................................................................................ 7

2.3. Census and other non-spatial data................................................................................................................ 7 2.3.1. Agricultural statistics (FAOSTAT, FAOAQUASTAT, IFA) ............................................................... 7 2.3.2. Crop calendar data and documents (USDA, MARS, Crop and Country calendars) ............................. 8

2.4. Interpreted data or modeling outputs ........................................................................................................... 8 2.4.1. Soil data (WISE) .................................................................................................................................. 9 2.4.2. Land use and land management data (GLU, Manure data) .................................................................. 9 2.4.3. Weather data (Tyndall) ......................................................................................................................... 9

3. Reference for geographical representation of the data ...................................................................................... 11 3.1. Primary geographical reference grid .......................................................................................................... 11

3.1.1. 5‟ spatial resolution geographical grid (Global grid) .......................................................................... 11 3.2. Secondary geographic reference ................................................................................................................ 12

3.2.1. Homogenous response units (HRU) ................................................................................................... 12 3.2.2. 30‟ spatial resolution geographical grid (PX30) ................................................................................. 14 3.2.3. Administrative units (COUNTRY) .................................................................................................... 14

3.3. Landscape units and simulation units ........................................................................................................ 15 3.3.1. Landscape units (HRU*PX30 zone, SimU delinetion) ...................................................................... 16 3.3.2. Simulation units (SimU) ..................................................................................................................... 16

4. Input data interpretation for global database ..................................................................................................... 17 4.1. Land cover and land use data ..................................................................................................................... 17

4.1.1. Land cover statistics ........................................................................................................................... 17 4.1.2. Land use statistics ............................................................................................................................... 18 4.1.3. Land cover/land use relevance and visulisation mask ........................................................................ 18

4.2. Topography and soil data ........................................................................................................................... 20 4.2.1. Topography data ................................................................................................................................. 20 4.2.2. Analytical characteristics of soil ......................................................................................................... 21

4.3. Cropland management data ....................................................................................................................... 23 4.3.1. Selection of crops for global database ................................................................................................ 24 4.3.2. Crop share and crop rotation rules data .............................................................................................. 25 4.3.3. Crop calendar data .............................................................................................................................. 27 4.4.4. Fertlization rates ................................................................................................................................. 30

4.5. Weather data .............................................................................................................................................. 31 5. Global database data structure........................................................................................................................... 32

5.1. Spatial reference database (SpatialReference_v10) ................................................................................... 32 5.2. Land cover and Land use database (LandCover&LandUse_v10) ............................................................. 34 5.3. Topography and soil database (Topography&Soil_v10) ........................................................................... 37 5.4. Cropland management database (CroplandManagement_v10) ................................................................. 42 5.5. Climate database (Climate_v10) ................................................................................................................ 47

References ............................................................................................................................................................. 50 Appendix 1: Input data requirements of the EPIC model ..................................................................................... 53 Appendix 2: Selected sources of national crop calendar data ............................................................................... 55 Appendix 3: SQL codes of select queries for preview and export the data ........................................................... 56

Page 3: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

3

1. Introduction

Global database for bio-physical modeling (further referred as global database)

described here has been created within 7th

EC framework program project GEO-BENE

(Global Earth Observation – Benefit Assessment: Now, Next, and Emerging, http://www.geo-

bene.eu/ ).

Global database is primarily supposed to support the data-model fusion approach in

agriculture sector modeling based on bio-physical model EPIC - Environmental Policy

Integrated Climate (Williams et al., 1995) and the data on soil, topography, climate, land

cover and land use available at global scale. Global database should serve also as a source of

data on land cover/land use for land use optimization modeling with FASOM model - Forest

and Agricultural Sector Optimization Model (Adams et al. 1996). Along with its main

purpose, the global database is also a tool for identification of the gaps in availability of

necessary global data for successful data-model fusion based interpretations in societal benefit

area (SBA) agriculture (Justice et Becker-Reshef, 2007) and this way to support the GEO-

BENE participation on a Group on Earth Observation (GEO) activities.

Data-model fusion based geographical modeling of landscape is not a straightforward

task. Depending on particular circumstances, it requires more or less complex approach to

gather all the necessary input information on landscape and natural or human-driven

landscape processes which is consequently used to carry out appropriate basis for (bio-

physical) model application and interpretation of the modeling results (c.f. Rossiter 2003). For

global-scale modeling, however, it is not possible to gather directly measured input data in

such a spatial, temporal and attribute detail which can fully satisfy the requirements of the

(bio-physical) model.

This implies that current version of the global database (version 1.0) can be considered

only a broad approximation to an optimal landscape model necessary for the fully successful

application of (bio-physical) model and rather than real requirements of (bio-physical) model

or complex landscape modeling it reflects a compromise between the needs and limits:

general modeling requirements – (needs) - a) global-scale geographical landscape

modeling, b) implementation of base-run and alternative scenarios for climate, land cover

and (arable) land management into the modeling, c) (optional) clustering of EPIC and

FASOM model within a complex landscape model (input/output data communication

between individual models);

input data requirements of the models – (needs) - a) quantitative data organized within

global coverage of landscape units which are homogenous as for topography, soil, climate

and management is essential to run the EPIC model (for more information on EPIC model

data requirements see the APPENDIX 1), b) statistical data for selected statistical units

(country-level administrative units further stratified by topography and soil) on area

portion of land cover/land use categories and/or average values of environmental

indicators coming from EPIC modeling are required by the FASOM model;

global data availability and quality – (limits) - a) thematic data is of various origin:

geographic data directly based on earth observations (interpreted/non-interpreted), digital

thematic maps, census data and other geographical data coming from complex

interpretations or modeling, b) data quality varies significantly across the available data:

spatial, attribute and temporal resolution of the data, its thematic relevance and general

accuracy and reliability, c) data accuracy and reliability may decrease after necessary

Page 4: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

4

estimations of missing data based on existing data and/or expert knowledge is done, d)

simplifications of general modeling concept due to the missing data without the possibility

of its estimation from existing data sources is necessary;

data harmonization – (limits) - common spatial reference and spatial resolution have to

be set for the data of different source and quality and different way of the data spatial

referencing (geo-referencing or geo-coding) to the common spatial reference may lead to

loose of information.

Page 5: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

5

2. Primary data identification – inputs for the global database

The list of the data sources given below is a result of internet and literature search

conducted under the first stages of the GEO-BENE project. The aim of this was to gather the

information on the best data available globally which could satisfy the model (and modeling)

requirements (for more particular information on EPIC model data requirements see the

APPENDIX 1). All of the data sources listed below were directly used for compiling the

global database; identification and description of other data sources without such relation to

global database would go beyond the scope of this report.

The final input data selection for models (and modeling) followed an assumption of

the data sources used for global database should represent a sample of data available from

public domains and any special contracts or other restrictions except the copyright or easy-to-

get licenses are not needed to get and use the data. Most of data can be directly downloaded in

digital form using on-line mapping and downloading services; some of the data have to be

requested from the data authorities, whereas some others represent only a hardcopy

publications.

Categorization of the data sources we introduce here should emphasize the differences

in the way of the data acquisition (data gathering and data processing methods, mapping

methods) for this could have significant influence on the quality and accuracy of the

information coming from the data interpretations as well as it can better indicate what global

earth observation data are up to hand for the SBA agriculture data-model fusion based

modeling.

2.1. Global earth observations based digital data

This group represents the data coming directly from earth observation systems and

earth observation data only slightly processed or interpreted. Data is originally in raster

format with various spatial resolutions (e.g. space or air-born observations) or in form of to

single or multi- point related measurements (e.g. weather station data).

2.1.1. Digital elevation data (SRTM, GTOPO30)

The high-resolution global Shuttle Radar Topography Mission digital elevation model

(further referred as SRTM) derived by NASA (http://www2.jpl.nasa.gov/srtm/) was used as a

source of global elevation data. SRTM digital elevation model is available in 3‟‟ horizontal

resolution (approximately 90 m at the equator) for areas between the latitudes from 60 N to 60

S, the altitude measure units are meters above a sea level.

Global 30 Arc Second Elevation Data (further referred as GTOPO30) (http://edc.usgs.

gov/products/elevation/gtopo30/gtopo30.html) was used as a source of global elevation data.

GTOPO30 is a global digital elevation model available in 30‟‟ horizontal resolution

(approximately 1 km at the equator); the altitude measure units are meters above a sea level. It

was derived from several raster and vector sources of measured and pre-processed

topographic information. GTOPO30, completed in late 1996, was developed over a three year

period through a collaborative effort led by staff at the U.S. Geological Survey's EROS Data

Center.

Page 6: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

6

2.1.2. Land cover data (GLC2000)

The Global Land Cover for year 2000 (http://www.gvm.jrc.it/glc2000/defaultGLC

2000.htm) produced as a common activity of several national and international institutions

coordinated by JRC (further referred as GLC2000) was used as a basic source of land cover

information at global scale. GLC2000 global raster is available in spatial resolution of

approximately 32‟‟ (approximately 1 km at the equator). Information on spatial distribution of

21 land cover classes interpreted from SPOT 4 VEGETATION 1 program satellite imagery

(http://www.cnes.fr/web/1468-vegetation.php) using Land Cover Classification System of

FAO (Di Gregorio et Jansen 2000) is available from GLC2000 legend (Table 2.1).

Table 2.1: Legend to Global Land Cover 2000 (GLC2000)

LAND COVER CLASS CLASS DESCRIPTION

1 Tree Cover, broadleaved, evergreen

2 Tree Cover, broadleaved, deciduous, closed

3 Tree Cover, broadleaved, deciduous, open

4 Tree Cover, needle-leaved, evergreen

5 Tree Cover, needle-leaved, deciduous

6 Tree Cover, mixed leaf type

7 Tree Cover, regularly flooded, fresh water

8 Tree Cover, regularly flooded, saline water

9 Mosaic: Tree Cover / Other natural vegetation

10 Tree Cover, burnt

11 Shrub Cover, closed-open, evergreen

12 Shrub Cover, closed-open, deciduous

13 Herbaceous Cover, closed-open

14 Sparse herbaceous or sparse shrub cover

15 Regularly flooded shrub and/or herbaceous cover

16 Cultivated and managed areas

17 Mosaic: Cropland / Tree Cover / Other natural vegetation

18 Mosaic: Cropland / Shrub and/or grass cover

19 Bare Areas

20 Water Bodies

21 Snow and Ice

22 Artificial surfaces and associated areas

23 No data

2.1.3. Weather data (ECWMF)

The European Centre for Medium-Range Weather Forecasts, Reading, United

Kingdom provides an integrated weather forecasting system based on of processing manifold

earth observation data (http://www.ecmwf.int/products/). Grid of 2.5°spatial resolution data

on daily weather (further referred as ECWMF) was used to calculate monthly statistics

required by EPIC weather generator.

Page 7: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

7

2.2. Digital thematic maps

This group represents digital versions of hardcopy maps created by classical mapping

methodologies or other digital thematic maps which has not been directly processed on the

basis of global earth observations data. Data is originally available in vector format (polygon

or polyline data).

2.2.1. Administrative regions (GAUL)

Global Administrative Regions Layer version 2007 () (http://www.fao.org/geonetwork

/srv/en/metadata.show?id=12691&currTab=simple, further referred as GAUL) processed

under the authority of the FAO and European Commission was used as a source of the

country-level administrative regions data. Countries are in GAUL vector layer identified both

by country name and country code which can be easily compared with official United Nations

coding list of countries and world regions (http://unstats.un.org/unsd/methods/m49/

m49.htm).

2.2.2. Soil data (DSMW)

The digital version of the 1:5 000 000 scale Soil map of the world (further referred as

DSMW) version 3.6 (http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116&curr

Tab=simple) was used as a source of data on distribution of major soil units across the world.

DSMW soil mapping units delineations (available both in vector or 5 arc minutes resolution

raster) are attributed with information on soil mapping unit soil components (soil typological

units and soil phases) and information on their area portion (%) of the soil mapping unit

delineation. Totally, information on 106 soil typological units classified according to map

legend (FAO-UNESCO 1974) and 5 miscellaneous non-soil units (glaciers, inland waters,

dune and shifting sands, rock debris and outcrops, salt flats) can be retrieved from the map.

2.3. Census and other non-spatial data

This data group represents mostly the statistical data related (geo-coded) to statistical

administrative units (country and first sub-country). Some of the data is a result of a slight

interpretation or aggregation of original statistics. Data is originally digital attribute data

organized in the tables; some data is in form of hardcopy publications (plain text, tables).

2.3.1. Agricultural statistics (FAOSTAT, FAOAQUASTAT, IFA)

FAO on-line data server (further referred as FAOSTAT) provides vide range of

country specific agricultural statistics. FAOSTAT served a source of 1961 to 2006 years

country-level statistics on crop harvested areas (http://faostat.fao.org/site/ 567/default.aspx)

and 2002 to 2005 years statistics on total consumption of nutrients (N, P2O5, K2O) for selected

countries (http://faostat.fao.org/site/575/default.aspx).

Website AQUASTAT maintained by FAO (further referred as FAOAQUASTAT)

provides specific information on water management in agriculture, including irrigation areas

statistics and water withdrawal by agriculture statistics. Country and crop specific irrigation

calendar for the 90 countries of the world was used for global database as the source

information on start and end days of crop planting and harvesting (http://www.fao.org

/nr/water/aquastat/main/index.stm).

Page 8: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

8

International Fertilizer Industry Association provides statistics on country and crop

specific fertilizer consumptions (further referred as IFA). IFA dataset

(http://www.fertilizer.org /ifa/statistics.asp) was used as a source of data on crop and country

specific fertilizer application rates and total country-level average nutrients consumption (N,

P2O5, K2O).

2.3.2. Crop calendar data and documents (USDA, MARS, Crop and Country calendars)

Climate and crop calendar for the key producing regions and countries is available

from U.S. Department of Agriculture document (USDA 1994, further referred as USDA). In

the publication concentration zones of major crops for each country are identified and

completed with information on historical averages of crop area (ha), yields (t/ha) and

production (t). Coarse grain, winter and spring wheat, barley, rice, major oilseeds, sugar crops

and cotton are included in USDA publication.

Crop calendars, agriculture practices calendars, crop rotations and other information

on selected crops for the part of Europe (Estonia, Latvia, Lithuania, Poland, Czech Republic,

Slovakia, Hungary, Slovenia, Romania, Bulgaria and Turkey) are available from publications

of Kučera et Genovese 2004a, 2004b, and 2004c (further referred as MARS dataset). Wheat,

barley, maize, rice, sugar beet, sunflower, soya bean, rape, potato, cotton and olive for Italy,

Spain and Greece are given by Narciso et al. (1992). Some other sources of national crop

calendar data used for global database are listed in APPENDIX 2.

Crop harvest calendars for sugar beet (FAO, 1959a), sugarcane (FAO, 1959b) and

coffee (FAO, 1959c) include selected countries of the world. Rice crop calendar

(http://www.irri.org/science/ricestat/) contains the data on planting and harvest dates (months)

of rice for selected countries of the world. The crop calendar (planting and harvesting dates)

of winter and spring wheat for Albania, Austria, Bulgaria, Czech Republic, Finland, Hungary,

Norway, Poland, Romania, Slovak Republic, Sweden, Switzerland, Ex-Yugoslavia is

available from publication of Russell et Wilson (1994). Autumn or winter and spring barley

crop calendar (planting and harvesting dates) for Belgium, Denmark, France, Germany,

Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain and United Kingdom is

available in publication of Russell (1990)

The potato crop calendar (MacKerron 1992) includes dates of planting and harvesting

(decade and month) for Germany, France, Italy, Netherlands, Belgium, United Kingdom,

Ireland, Denmark, Greece, Spain and Portugal. Information about potato and sweet potato

cultivation with emphasis on developing countries collects International Potato Centre. On

their web-site are presented the potato crop calendar for countries of Africa, South and Middle

America and Eurasia (http://research.cip.cgiar.org/confluence/ display/wpa/Home). The crop

calendar of sweet potato is available for the countries of Africa and Asia (http://research.cip.

cgiar.org/confluence/display/WSA/Home ).

2.4. Interpreted data or modeling outputs

This group represents the data coming from various interpretations based on existing

data and expert knowledge or data coming from modeling (interpolations results, statistical

down-scaling results, etc.). Such a data represents secondary data not directly measured or

collected. The interpretation methodologies used for data creation influence significantly the

data accuracy and reliability. Various digital geo-referenced or geo-coded data available in

this group have various spatial resolutions and is available in raster or vector representation

depending on the data origin.

Page 9: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

9

2.4.1. Soil data (WISE)

International Soil Reference and Information Centre (ISRIC) 5 by 5„ grid of soil

properties estimation based on global soil distribution (DSMW) and soil profile data (WISE

soil profile database, http://www.isric.org/UK/About+Soils/Soil+data/Geographic+data/Glo

bal/Global+soil+profile+data.htm) interpretation (further referred as WISE, http://www.isric.

org/UK/About+Soils/Soil+data/Geographic+data/Global/WISE5by5minutes.htm) was used as

a source of DSMW soil typological unit specific data on soil analytical properties for 5 depth

intervals of soil profile (20 cm intervals for total depth of 1m). Detailed interpretation

methodology for WISE compilation is described in publication of Batjes (2006).

2.4.2. Land use and land management data (GLU, Manure data)

Global crop distribution data processed by International Food Policy Research

Institute was used as a source of basic information on land cover and land use. The data

(further referred as GLU) represents a global coverage of a regular-shaped statistical units (5‟

spatial resolution grid) attributed with estimated crop cultivation and harvest areas (physical

area in ha) and crop production (tons/ha) for 20 globally most important crops or crop groups

(wheat, rice, maize, barley, millet, sorghum, potatoes, sweat potatoes and yams, cassava,

bananas and plantains, soybean, other pulses, sugar cane, sugar beet, coffee, cotton, other

fiber crops, groundnuts, other oil crops). Estimations are done separately for the four

agricultural production systems (high input - irrigated, high input - rainfed, low input - rainfed

and subsistence management systems). Final dataset resulted from downscaling of many

national and sub-national agricultural census data using additional spatial information on land

cover (GLC2000), crop suitability and management system potential yields (agro-ecological

zones assessment data, Fischer et al. 2000), irrigation areas (Global irrigation map, Siebert et

al, 2007) and population density data (Gridded Population of the World, http://sedac.ciesin

.columbia.edu/gpw/index.jsp). All the methodological details are given in the publication of

You et Wood (2006). Actually, GLU dataset is not available for download by any public on-

line service. For global database GLU data set was provided by IFPRI after personal

communication with Dr. Liangzhi You.

Global scale 5‟ spatial resolution on nitrogen rates coming from manure application

(further referred as manure data) compiled by Liu et al. (in press) were used as a source

dataset on nitrogen fertilization and total manure application. Manure data was interpreted

from global data on livestock density (Gridded Livestock of the World,

http://www.fao.org/ag/AGAinfo/resources/en/glw/default.html) developed by FAO in

collaboration with the Environmental Research Group Oxford and FAOSTAT data on country

and livestock specific data on slaughter weights using published knowledge on nitrogen rates

in livestock excretion and amounts of manure recycled to cropland. Actually, manure dataset

is not available for download by any public on-line service. For global database the dataset

was provided after personal communication with Dr. Junguo Liu.

2.4.3. Weather data (Tyndall)

The Tyndall Centre for Climate Change Research of University of East Anglia,

Norwich, UK provides data on historical time series of global weather for the period from

1901 – 2000 and 16 climate change scenarios (further referred as Tyndall, http://www.cru.

uea.ac.uk /~timm/grid/TYN_SC_2_0.html). Global scale land surface coverage 0.5°spatial

resolution grids of modeled climate data on cloud cover, diurnal temperature range,

Page 10: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

10

precipitation, temperature, vapor pressure were compiled from several interpolated global and

regional climate datasets. Methodological details of Tyndall data are described in publication

of Mitchel et al. (2004).

Page 11: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

11

3. Reference for geographical representation of the data

Three-level hierarchical system of geographical objects was designed for spatial

referencing the global database data on natural and management landscape characteristics. It

comprises primary geographical reference grid (chapter 3.1), secondary geographical

reference objects (HRU, 30‟ spatial resolution grid and country-level administrative unit,

chapter 3.2) and tertiary reference objects (landscape units, chapter 3.3). Database reference

objects (simulation units, chapter 3.3) provide the least non-spatial data reference in the global

database.

3.1. Primary geographical reference grid

Primary geographic reference is the basic spatial frame for geographical representation

and visualization of all the data stored in global database. It is a basis for spatial referencing

of secondary and tertiary reference objects. Moreover, primary geographic reference is the

basic harmonization tool for the geographical data coming from various sources.

3.1.1. 5’ spatial resolution geographical grid (Global grid)

Global extent (-90° S to 90° N and -180° W to 180° E) grid designed in geographical

coordinate system WGS 84 with pixel resolution of 5‟ (about 10 X 10 km on equator) was

created to serve the primary geographical reference for the global database (further referred as

global grid). Land surface mask was used to keep only those pixels of global grid which

represent land surface (any other pixels representing oceans are not relevant for global

modeling). Land surface mask resulted from intersection of the land surface subsets of the

most significant geographical data inputs (GTOPO30, SRTM, DSMW, GLC2000, GAUL,

IFPRI-GLU) harmonized in global grid (chapter 3.2.). Decision was done also to exclude

Antarctica from global grid due to the most of its area is covered by ice or bare rocks. Totally,

global grid comprises 2.186.775 pixels.

Each grid cell of the global grid is indexed by column and row number counted from

upper left corner of the grid (point coordinates 90° N, -180° W). Column numbers ranges

from 1 to 4320, row numbers ranges from 1 to 2160. Independently from column-row

indexing each pixel is indexed also by x and y coordinate (decimal degree) of pixel centroid.

Point lattice can be then displayed via centroid coordinates to visualize global grid in a map.

This makes global grid independent of a particular GIS platform used for compilation and

storage of the data as well as it supports the exchange of geospatial data between various GIS

or database systems via simple and highly interoperable ASCII text files (e.g. txt, csv).

Classical database tools can be used for storage, maintenance and analyzing the data geo-

referenced to point lattice and GIS support is needed only for displaying the data

geographically in a map.

Modeling requires that all the data on area of spatial units is reported as real area

values (e.g. ha or km2). Real area (ha) for each pixel of global grid was calculated as a 1/36 of

real area of corresponding 30‟ resolution grid pixel (chapter 3.2.2). Real area of 30‟ resolution

grid resulted from transformation of the geographically not projected data (WGS84 coordinate

system) into the equal area geographic projection system (Goode-Homolosine projection)

following the routine described by Lethcoe and Klaver (1998).

Page 12: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

12

3.2. Secondary geographic reference

Secondary geographic reference objects provide spatial reference for all the data direct

referencing of which to the global grid is not advantageous or possible (chapter 3.2.2, 3.2.3)

Secondary reference object also serve as data stratification tool in bio-physical and

optimization models communication (chapter 3.2.1). Along with referencing function

secondary spatial reference objects play important role in the delineation of landscape units

(chapter 3.3., Fig. 3.2).

Particular secondary geographic reference object is spatially referenced to global grid

as an attribute value assigned to relevant pixel or group of pixels. It can be geographically

displayed on a map as a spatial zone of global grid (Fig. 3.1).

Fig. 3.1: Secondary geographical reference object zone (e.g. HRU) visualized via global grid.

3.2.1. Homogenous response units (HRU)

Concept of homogenous response units (HRU) used here was adopted after slight

modification from earlier works (Schmid et al. 2006, Balkovič et al. 2006, Stolbovoy et al.

2007) as a general concept for delineation of basic spatial units. Only those characteristics of

landscape, which are relatively stable over time (even under climate change) and hardly

adjustable by farmers, were selected. HRU is a basic spatial frame for implementation of

climate-change and land management alternative scenarios into global modeling and therefore

it is one of basic inputs for delineation of landscape units (chapter 3.4, Fig. 3.4). Moreover,

HRU provides a possible interface for communication of bio-physical and optimization

models (EPIC model derived and consecutively HRU level aggregated information on

environmental indicators can input FASOM optimization modeling).

Tab. 3.2: Altitude, slope and soil class criteria for HRU delineation

LAND CHAR. UNIT CLASS (CLASS INTERVAL)

altitude meters 1 (0 – 300), 2 (300 – 600), 3 (600 – 1100), 4 (1100 – 2500), 5 (> 2500),

slope inclination degree 1 (0 – 3), 2 (3 – 6), 3 (6 – 10), 4 (10 – 15), 5 (15 – 30), 6 (30 – 50), 7 (> 50)

soil - 1 (sandy), 2 (loamy), 3 (clay), 4 (stony), 5 (peat), 88 (no-soil)

HRU is spatially delineated as a zone of global grid having same class of altitude,

slope and soil (HRU class definitions are listed in Tab. 3.2.):

Dominant altitude class was calculated by raster algebra as a zonal majority value of pre-

classified GTOPO30 raster altitude class over a one global grid pixel area;

Page 13: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

13

Dominant slope class was calculated by raster algebra as a zonal majority value of pre-

classified 30‟‟ spatial resolution temporary raster slope class over a one global grid pixel

area. Temporary raster used for calculations was interpreted from original SRTM and

GTOPO30 data as follows. The SRTM data calculated slopes at 3” spatial resolution were

grouped into the classes 0°- 3°, 3°-6°, 6°-10°, 10°-15°, 15°-30°, 30°-50° and >50°. For the

30” resolution raster zonal majority procedure was done to get the 60 N to 60 S extent

raster of slope classes. To fill up the missing regions from 60°N to 90°N and 60°S to 90°S

a slope raster with the GTOPO30 was calculated. The region 60°N to 60°S was covered

by both SRTM and GTOPO30 derived slope data. This overlapping region was used to

create a look-up table which allowed transforming the slope from the GTOPO30 to the

slope class shares of the SRTM and fill up the missing regions.

Dominant soil class represents most frequent soil class of DSMW soil mapping unit (as

for its relative area) assigned to global grid pixel by intersection (spatial join) of global

grid centroid lattice and original DSMW layer. Soil typological units of the particular

DSMW soil mapping unit were classified into five pre-defined soil classes. Based on

WISE soil profile data on aggregated soil texture classes (coarse, medium and heavy

texture) sandy, loamy and clay soil classes were interpreted; soil typological units

classification was applied for stony and peat soil classes interpretation. An arbitrary value

of 88 was assigned to all non-soil bodies. Sum of the areas of all soil typological units

classified to the same soil class or sum of areas of all non-soil bodies having dominant

area portion of the total DSMW mapping unit area was then applied as a criterion for

global grid pixel dominant soil class.

Fig. 3.2: Global HRU coverage.

Altitude, slope and soil class value assigned to 5‟ spatial resolution pixel represents

spatially most frequent class value (not average!) taken from input data of higher spatial

(GTOPO30, SRTM) or attribute (DSMW) resolution than target dataset (i.e. idea of “the most

likely” natural conditions is adopted here). This implies that not absolute information on

landscape quality and variability over the 5‟ spatial resolution pixel area is transferred to

global grid in data harmonization process and resulting harmonized information used for

HRU delineation is just broad approximation to real variability of the global landscapes.

Page 14: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

14

Totally, 150 unique combinations of altitude, slope and soil class resulted from HRU

delineation process (Fig. 3.2). Each delineated HRU zone is indexed by numerical code

assembled from code of altitude, slope and soil on first, second and third position,

respectively.

3.2.2. 30’ spatial resolution geographical grid (PX30)

Overlay of global grid and global extent (-90° S to 90° N and -180° W to 180° E) 30‟

spatial resolution grid (about 50 X 50 km on equator) designed in geographical coordinate

system WGS 84 was done to get regularly-shaped zones of global grid indexed by column and

row index of underlying 30‟ spatial resolution grid (column and row indexes counted from

upper left corner, coordinates 90° N, -180° W). Each zone was also assigned with x and y

coordinate values of corresponding 30‟ resolution pixel centroid.

Fig. 3.3: Real area change with increasing latitude

Real area (ha) of 30‟ resolution grid was calculated after transformation of the

geographically not projected data (WGS84 coordinate system) into the equal area geographic

projection system (Goode-Homolosine projection) following the routine described by Lethcoe

and Klaver (1998). Real area (ha) change with latitude is shown on a map (Fig. 3.3).

30‟ resolution grid zone (further referred as PX30) provide a direct geographical

reference for interpolated weather data (Tyndall) as well as it is an arbitrary spatial frame

which is to secure local detail in geographical analyses and interpretation of the input data for

bio-physical modeling (topography and soil data, land cover and land use statistics).

3.2.3. Administrative units (COUNTRY)

Simple overlay (spatial join) of GAUL data and the global grid centroid lattice was

done to get country and first sub-country level administrative unit identification (country

code) for all pixels of global grid. Consequently, original country-level administrative unit

codes taken from original GAUL dataset were replaced by a country code from official UN

country list. UN country codes from ad-hoc constructed look-up table (Tab. 3.1) were

Page 15: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

15

assigned to all administrative regions having no country information in GAUL dataset

(geopolitically controversial administrative regions). Original GAUL coding was kept only

for sub-country-level administrative regions.

Country level administrative regions (further referred as COUNTRY) provide a spatial

reference for geo-coding the most of national-level agricultural census data and national or

national level data on crop management calendars. Sub-country-level administrative regions

provide an additional spatial reference for geo-coding the sub-national crop management

calendar data for selected countries (chapter 4.3.3).

Tab. 3.1. UN country codes for geopolitically controversial administrative regions.

GAUL REGION (COUNTRY

LEVEL)

UN COUNTRY CODE (COUNTRY NAME)

Aksai Chin 356 (India)

Arunashal Pradesh 356 (India)

Dhekelia and Akrotiri SBA 196 (Cyprus)

Gaza Strip 376 (Israel)

Hala'ib triangle 818 (Egypt)

China/India 156 (China)

Ilemi triangle 736 (Sudan)

Jammu Kashmir 356 (India)

Kuril islands 643 (Russian Federation)

Madeira Islands 620 (Portugal)

Ma'tan al-Sarra 736 (Sudan)

West Bank 376 (Israel)

3.3. Landscape units and simulation units

Landscape units and simulation units provide immediate reference for the global

database data displaying in geographical or classical database. Simple scheme illustrating the

definition and position of these units in global database is given in Fig. 3.4.

Fig. 3.4: Landscape zones and simulation units – definition, functional position and

referenced data

Page 16: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

16

3.3.1. Landscape units (HRU*PX30 zone, SimU delinetion)

Landscape units were implemented to satisfy the bio-physical model requirements for

spatial units being homogenous as for its natural conditions and landscape management.

Landscape units are the smallest spatial units coming from intersect of HRU, PX30 and

COUNTRY spatial delineations. The function of landscape units in global modeling is to

provide both the immediate reference to the data required as inputs for bio-physical model

(Fig. 3.4) and to provide the least possible spatial delineation (which can be further specified

only on non-spatial level, chapter 3.3.2). Two hierarchical levels of landscape units are

assumed in global modeling:

HRU*PX30 zone (Fig. 3.4.) is delineated as the smallest landscape body homogenous as

for its natural conditions (topography, soil and weather). HRU*PX30 zone provides direct

spatial reference for EPIC input data on soil and topography (average altitude,

representative slope and soil typological unit specific analytical values);

Simulation unit delineation (further referred as SimU delineation, Fig. 3.4, Fig. 3.5) is

the HRU*PX30*COUNTRY zone delineated as the smallest landscape body homogenous

as for its natural conditions (the same as above) but with further reference to a particular

country-level administrative region. SimU delineation provides direct spatial reference for

non spatial information on land cover and land use. Maximal area of SimU delineation is

equal to an area of one 30‟ spatial resolution grid pixel and its area decrease from about

300 000 ha on equator to about 30 000 ha in high latitudes; minimal area of SimU

delineation is equal to an area of one 5‟ spatial resolution grid pixel and its value decrease

from about 8 500 ha on equator to about 950 ha in high latitudes.

Fig. 3.5: SimU delineation visualized as a global grid zone.

3.3.2. Simulation units (SimU)

Simulation unit (further referred as SimU) is non-spatial semantic unit which

represents one of all possible land cover, land use (for cropland) and via this two also land

management alternatives which can take place in the area defined by SimU delineation (Fig.

3.4.). In the global database SimU represent a unit which bears definite information on unique

natural and management condition existing at particular place. This information is utilized in

bio-physical modeling to assemble the set of management alternative specific input files for

model runs. SimU provides also a reference for interpretation and displaying the bio-physical

modeling outputs.

Page 17: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

17

4. Input data interpretation for global database

4.1. Land cover and land use data

4.1.1. Land cover statistics

Combined GLC2000 and GLU datasets interpretation was done to get global grid and

SimU delineation specific data on cropland area necessary for setting up the base-run

scenarios for agro-ecosystem modeling (information absent in GLC2000) and data on other

land cover classes area (grasslands, forests, wetlands, other natural vegetation) necessary for

land cover/land use optimization modeling (information absent in GLU).

Table 4.1: GLC2000 classes derived global database land cover classes

GLOBAL DATABASE LAND COVER CLASS ORIGINAL GLC2000 CLASSESS (Tab. 2.1)

total agricultural land 16,17,18

grassland 13,

forest 1, 2, 3, 4, 5, 6, 9, 10

wetlands 7, 8, 15

other natural vegetation 11, 12, 14

not relevant land covers 19, 20, 21, 22

Zonal analyze of pre-classified GLC2000 layer (Tab. 4.1.) over the global grid was

done to get pixel specific statistics on total cropland, grassland, forest, wetland, other natural

vegetation and not relevant land cover classes real areas (ha). Pixel specific cropland area (ha)

was calculated from original GLU data by summing up all particular crop areas over all

assumed management systems. Consistence of GLU and GLC2000 data was checked by

subtracting GLU derived cropland area from GLC2000 derived total cropland area. Result of

the consistence check is shown on a map (Fig. 4.1).

Fig. 4.1: GLU and GLC2000 land cover data inconsistence.

Page 18: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

18

There is lot of pixels having GLU cropland area higher than GLC2000 total

agriculture area; even pixels having higher GLU cropland area than total pixel area were

identified in dataset. Reason for that resides in an inconsistence of GLC2000 and national

agricultural census data (more agricultural land is reported in census than it is available in

GLC2000) as well as in GLU model total area of all pixels was arbitrary set to 9000 ha which

have could caused some of the inconsistencies (personal communication with Dr. Liangzhi

You).

It was decided that GLU cropland area should be secured in final harmonized land

cover dataset (cropland land cover class) with only exception in the case of GLU cropland

area is higher than total pixel area; and data on all other land cover classes were adjusted

following several simple rules:

if the GLU derived cropland area was less than GLC2000 derived total agricultural

land area an additional land cover class (other agricultural land) was introduced and

assigned with area value calculated as a difference of GLC2000 total agricultural land

area and GLU cropland area, GLC2000 derived areas of all other land cover classes

were kept;

if the GLU derived cropland area was more than GLC2000 derived total agricultural

land area additional land cover class (other agricultural land) was given by zero value

and areas of all other land cover classes were recalculated based on their area portion

of global grid pixel so that resulting sum of their areas was equal to difference of total

pixel area and GLU reported cropland area;

if the GLU derived cropland area was more than total pixel area; cropland area value

was arbitrary set to total pixel area and areas of all other land cover classes were set to

zero.

For each pixel of global grid a metadata field was created to indicate the method used

for land cover data harmonization. Finally, harmonized land cover dataset was spatially

aggregated by zonal sum of all land cover classes over the SimU delineation zone to get SimU

delineation specific data on land cover.

4.1.2. Land use statistics

Information on cropland land use classes (chapter 2.4.2) is available from GLU

dataset. Pixel specific cropland management system area (ha) was calculated from original

GLU data by summing up all crop areas over particular management system. Additional area

adjustments based on management system area portion of total cropland were done in case of

GLU dataset derived specific cropland area was different from cropland area of harmonized

land cover dataset. Finally, spatial aggregation of land use dataset was done by zonal sum of

all land use classes over the SimU delineation zones to get SimU delineation specific data on

land use.

4.1.3. Land cover/land use relevance and visulisation mask

An arbitrary 5% threshold of land cover area portion of total SimU delineation area

was set to tell whether SimU delineation is relevant or not for simulation of the defined land

cover alternative (i.e. which SimU are to be defined across the SimU delineation area).

Similarly, a relevance of cropland land use alternative for simulation was set based on 5%

Page 19: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

19

threshold of cropland management system area portion of total cropland area. Relevance

masks were set for to limit bio-physical model runs only to most representative management

alternatives across the SimU delineation area. Relevance masks are not mutually exclusive

and in a case it meets the threshold more than one land cover/land use alternative can be

assumed for the SimU delineation. Absolute SimU delineation relevance mask based on

spatial dominance of particular cropland management system across the cropland was in

addition set only for cropland land use alternatives (chapter 4.3.2).

Fig. 4.1: Visualization mask for land cover (land use) data

SimU delineation is the least spatial object which can be visualized on the map and all

related information on land cover/land use is organized only as a non-spatial statistics (chapter

3.4.). To overcome this problem, for visualization of the SimU specific data (input/output data

for bio-physical model, management alternatives spatial pattern, etc.) a visualization mask

was defined for each of the global grid pixels.

If a particular land cover/land use alternative is considered for visualization, only

those pixels of global grid are visualized which represents relevant SimU delineation zone and

which meet the 5% threshold of minimum land cover area portion of total pixel area or land

use area portion of total cropland area in particular pixel (Fig. 4.1, Fig. 4.2).

Fig. 4.2: Land cover/land use visualization mask – example of irrigated cropland

management system.

Page 20: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

20

4.2. Topography and soil data

4.2.1. Topography data

Zonal mean of altitude value (m) coming from GTOPO30 was calculated over the

HRU*PX30 zone to get average altitude as an input for bio-physical model (Fig. 4.3.).

Fig. 4.3: Global pattern of HRU*PX30 zone average altitude.

Fig. 4.4: Global pattern of HRU*PX30 zone representative slope.

Representative slope value for slope class coming from HRU (Tab. 4.2) was assigned

to HRU*PX30 zone after recalculation to percent by simple equation (slope in % = tg(slope

in deg) * 100) to get slope value required by bio-physical model (Fig. 4.4). Dominant slope

Page 21: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

21

class interval method derived representative value seems to be more appropriate as an average

value for the averaging of slope over the HRU*PX30 zone could result in non-realistic

(artificial) values in a case of wide range of slopes is presented within a zone.

Table4.2: Representative slope values assigned to the slope classes

HRU SLOPE CLASS INTERVAL REPRESENTATIVE SLOPE VALUE (DEG)

0 - 3 1

3 - 6 4

6 - 10 8

10 - 15 12

15 - 30 23

30 - 50 40

> 50 60

PX30 pixel centroid x and y coordinate values were assigned to all corresponding

HRU*PX30 zones as a representative value of landscape unit geographical position required

by bio-physical model. This simplification should not affect negatively the simulations.

4.2.2. Analytical characteristics of soil

Data on analytical characteristics of soil required by bio-physical model are to

HRU*PX30 zone related via information on dominant soil typological unit. A slightly

modified methodology firstly introduced by Batjes at al. (1995) for DSMW data interpretation

was applied to get HRU*PX30 dominant soil typological unit from DSMW data.

Fig. 4.5: Global pattern of HRU*PX30 zone dominant soil typological units (soils according

to FAO-UNESCO 1974), RK1, RK2, DS, and ST stands for no soil bodies, NA for non-

identified soil bodies.

Page 22: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

22

Area percentage of each DSMW soil mapping unit identified within the particular

HRU*PX30 zone extent was calculated in the first step using the soil mapping unit data

obtained from from intersect of global grid centroid lattice and original DSMW layer. In the

next step, soil typological unit specific relative areas (%) calculated from data on DSMW soil

mapping unit area percentage and data on DSMW soil mapping unit composition (chapter

2.2.2) were summed over the HRU*PX30 zone to get total area portion (%) for each of the

soil typological units. Soil typological unit of the highest area portion was selected as

dominant one for HRU*PX30 zone (Fig. 4.5).

Dominant soil typological unit relative area varies from 14 to 100% across the global

coverage of HRU*PX30 zones (Fig. 4.6.). This implies that in areas with high diversity of

soils (the case of dominant soil typological unit area is less than 60%) dominant soil

typological unit based concept cannot be considered more than just a broad approximation to

the appropriate model of soil variability.

Fig. 4.6: Area percentage of dominant soil typological unit of HRU*PX30 zone.

The most of the soil analytical data required by bio-physical model was adopted

directly from WISE dataset without or with only slight modifications such as transformation

of measurement units or re-calculation of relative to absolute values. Some other soil

analytical parameters (hydrological soil group and soil hydrological parameters, albedo of

moist soil) missing in WISE dataset were estimated from WISE data by available

methodologies or published knowledge.

Hydrological soil group was interpreted for each soil typological unit from WISE

analytical data on soil texture by simplified rules from official USDA-NRCS engineering

manual (USDA-NRCS 2007). Soil typological unit specific hydro-physical characteristics

were estimated from WISE analytical data on soil texture and bulk density by combined

methodology. Neural-network algorithm based pedo-transfer model Rosetta (Shaap et Bouten

1996) produced saturated hydraulic conductivity value and soil hydrological parameters

necessary for calculation of soil water content at field water capacity and wilting point by Van

Genuchten equation (Wösten et Van Genuchten 1988). An arbitrary value of 0,15 was

assigned to all soil typological units as albedo of moist soil representing median of the 0.1 –

0.2 albedo interval for dark soil surfaces given by Dobos (2006).

Page 23: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

23

All salt flats and shifting sands no-soil bodies (ST, DS) and all non identified soil

bodies (NA) which resulted from HRU*PX30 zone dominant soil typological unit analyze

were assigned by analytical values of other available soil typological units after the WISE

dataset rules (Batjes 2006). In a case of rock non-soil bodies (RK1, RK2) were identified as

dominant soil typological unit, analytical values of rankers or cambisols were given to stony

(RK2) or all other (RK1) HRU soil classes, respectively.

Analytical data on organic soils (histosols (O), dystric histosols (O), eutric histosols

(O, and gelic histosols (Ox)) are not available in WISE. Soil analytical parameters for organic

soils were estimated from the soil horizon data on three histosol soil profiles (Od) available in

global soil profile database compiled by ISRIC (Batjes 1995), WOFOST model default soil

input files for Europe (Boogaart et al. 1998) and published reference information on soils of

the world (FAO-UNESCO 1974, Dreissen at al. 2001) as follows.

Organic soils were assumed as being homogenous over the soil profile and therefore

the same analytical values were given to all soil profile depth intervals considered in WISE

dataset (five intervals to depth of 1 m). Median of soil albedo interval for dark soil surfaces

(0.15) given by Dobos (2006) was set for soil albedo parameter. Arbitrary value (0) was set to

volume of stones and calcium carbonate content soil parameters. For the presence of ground

water table close to the soil surface in most of organic soils (Driessen et al. 2001) and

relatively low value of saturated hydraulic conductivity taken from WOFOST input files soil

hydrological group was interpreted as D. Data on representative textural class of organic soils

was not available and it was arbitrary set for all organic soils to 30, 40 and 30 for sand, silt

and clay fraction, respectively, so that any soil textural extreme is eliminated from

simulations. Upper limit of bulk density interval for fibric histosols (0.15 t/m3) published by

Driessen et al. (2001) was given to all organic soils. Cation exchange capacity was calculated

as an average across the global soil profile database (Batjes 1995) and set for all organic soils

(95 cmol/kg). Base saturation was calculated as an average from global soil profile database

(25%) and together with cation exchange capacity value it was used for calculation of base

saturation for O, Od and Ox (23.75 cmol/kg). Representative value (75%) of eutric qualifier

base saturation interval (50-100%) was taken from DSMW legend (FAO-UNESCO 1974) and

used in calculation of base saturation for Oe (71.25 cmol/kg). Average soil pH (4.5)

calculated from global soil database (Batjes 1995) was given to O, Od and Ox organic soils.

Following both the representative soil pH for eutric histosols given by Driessen at al. (2001)

and FAO-UNESCO (1974) eutric histosols class definition soil pH parameter was set to 6.5

for Oe. Soil organic carbon was set to 35 % for all organic soils following the average

calculated from global soil profile database (Batjes 1995) and characteristics of histosols

given by Driessen et al. (2001). From WOFOST model default soil input files for Europe –

EC6 (Boogaart et al. 1998) representative values of 0.52, 0.13, and 5.62 were taken for field

water capacity, wilting point and saturated hydraulic conductivity, respectively.

4.3. Cropland management data

Country specific statistics on cultivated crop areas, crop rotation rules, crop

management calendar and data on fertilization and other supporting data (various crop

classifications and crop specific data) were compiled to satisfy the minimum set of EPIC

model requirements for crop management data (APPENDIX 1).

Page 24: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

24

4.3.1. Selection of crops for global database

GLU dataset crop list (chapter 2.4.2) was selected as the primary list of crops to be

included in global database. In the final list of crops (Tab. 4.1.) all crop groups included in

original GLU dataset (banana and plantains, sweet potato and yams, other pulses, other oil

crops, and other fiber crops) were replaced by country specific representative of the crop

group. The most frequent crop (as for its harvested area) in the country which was not

included in original GLU crop list was taken from FAOSTAT as the crop group

representative. If no crop was available for the country in FAOSTAT, the crop group

representative of the nearest country was taken.

Final list of crops covers only those crops which are considered globally most

important (You et Wood 2006) and many other crops with only regional significance is not

included (e.g. temporary pastures or silage corn for most of central and western Europe).

Tab. 4.1: List of crops assumed in global database.

CROP ACRONYM IN GLOBAL DATABASE CROP NAME

BARL barley

BEAN beans

CASS cassava

COFF coffe

COTT cotton

GROU ground nuts

MAIZ maize

POTA potatoes

RICE rice

SORG sorghum

SOYB soybean

SUGC sugarcane

WHEA wheat

JUTE jute

BHBE broad and horse bean

SUNF sunflower

SWTP sweet potatoes

BANA bananas

COCO coconuts

CPEA chick peas

FLAX flax fibre and tow

LENT lentils

LINS linseed

MELO melonseed

MILL millet

MUST mustard seed

OLIV olives

OPAL oil palm fruits

PEAS peas, dry

PLAN plantains

Page 25: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

25

CROP ACRONYM IN GLOBAL DATABASE CROP NAME

PULS pulses, nec

RAPE rapeseed

SAFF safflower seed

SESA sesame seed

SISA sisal

SUGB sugar beet

YAMS yams

4.3.2. Crop share and crop rotation rules data

Primary aim of crop share data is to provide a basis for compilation of the hypothetic

crop rotations or other crop cultivation strategies (such as plantations) which are to represent

the SimU delineation specific land use base-run scenario in bio-physical modeling. Because

of lack of relevant data on global scale, multiplied cropping during the year or multi-crop

cultivation (more than one crop at the same place and time) was not considered in standing

version of global database.

A SimU delineation and management system (high input, irrigated high input, low

input and subsistence) specific list of physical areas of all crops from final crop list (further

referred as crop share data) was constructed from original GLU data by zonal summing the

crop areas over the SimU delineation and consecutively refined to fit the SimU delineation

specific land use statistics data (chapter 4.1.2).

Tab. 4.2: Total duration of cultivation for selected perennial crops.

CROP TOTAL LENGTH OF CULTIVATION (years)

sugar cane 4

coffee 25

banana plant 25

plantain plant 25

sisal 15

coconuts palm 70

olive tree 100

oil palm 25

Relevance masks were interpreted and together with various crop classifications

included in crop share data table to make possible selection of various crop rotation options

which could optionally take a part in base-run scenarios set for SimU delineation:

Duration of crop cultivation less or more than 365 days was used as a criterion for

classification of all crops as annual or perennial. For selected perennial crops the most

likely total length of cultivation was interpreted from many available sources as given in

table (Tab. 4.2.);

Based on assumed crop cultivation strategies all crops were distinguished as crops

cultivated in rotation or as crops cultivated in monoculture (most of perennial crops and

typical plantain crops). For subsistence management system all crops (both annual and

perennial) were assumed as they are grown in rotation;

Page 26: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

26

SimU delineation relevance mask was taken from land cover/land use statistics dataset

(chapter 4.1.3) to indicate whether the management system is significant enough to be

considered in crop rotation or crop calendar base-run scenario for the particular SimU

delineation. Absolute relevance mask was interpreted based on SimU delineation specific

management systems areas data coming from land use statistics dataset (chapter 4.1.3) so

that only spatially dominant management system can be optionally selected as a

representative of the particular SimU delineation in bio-physical simulations;

Minimum crop area portion of total management system area in particular SimU

delineation was set to 5% and used as a criterion for setting up the crop relevance mask.

Crop relevance mask is to tell if crop is significant enough to be considered in base-run

scenario for SimU delineation;

In particular management system of some SimU delineations both crops grown in rotation

and monoculture crops can be relevant for simulation. Therefore an alternative mask was

interpreted to tell which crop cultivation strategy is more significant for particular

management system in SimU delineation. Alternative mask was defined based on total

area of monoculture crop or sum of all crops grown in rotation in particular SimU

delineation and management system.

Tab. 4.3: Crop rotation rules for global database, main crops in rows, preceding crops in

columns (0 – not possible, 1 – not suitable but possible, 2 – suitable and very suitable).

A crop rotation rules table compiled for Europe (Schmid et al. 2006) was after slight

modifications and completing of missing crops from final list of crop (Tab. 4.1) taken as the

Page 27: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

27

basis for global crop rotation rules table. Based on general knowledge on crop-after-crop

suitability in rotation and crop calendar data (chapter 4.3.3) a crop combination suitability in

rotation was classified into three classes as follows: i) very suitable to suitable (the crops are

usually grown in mutual combination), ii) not suitable but possible (crops are not usually

grown in mutual combinations or such combination is not recommended but there are not

physical restrictions such as overlapping planting/harvesting dates to cultivate crops in

combination), and iii) absolutely not suitable (physical restrictions such as overlapping dates

of planting/harvesting make crop combination in rotation impossible). Crop rotation rules as it

was set for global database are given in the table (Tab. 4.3).

4.3.3. Crop calendar data

A country and crop specific crop calendar from FAOAQUASTAT covering

developing countries in tropical and subtropical regions was used as a basic source of data on

starting and end decades for crop planting and harvesting. FAOAQUASTAT dataset was

completed with other available sources such as USDA document published global scale data

on crop management calendar, European regional data (MARS), and national (chapter 2.3.2,

APPENDIX 2) or crop specific (chapter 2.3.2) crop management calendar data and table of

country and crop specific start and end period of planting and harvesting was created. The

resulting table was compared against the country specific crop share data (chapter 4.3.2) and

all identified gaps in crop data were filled up by country specific crop calendar of crop with

similar cultivation requirements as missing crop (Tab 4.4) or by crop calendar of the same

crop from the nearest country.

Tab. 4.4: Rules for filling up the gaps in missing crops crop calendar data, intepreted from

IFA 1992 and Jurášek 1997, 1998 published data.

ALTERNATIVE CROP CROPS

maize sorghum, millet, sunflower

bean pea abd other pulses

soybean groundnut

jute groundnut

sunflower safflower, maize

Mostly for large countries but also for all other countries with heterogonous natural

conditions it is useful if crop calendar data can be interpreted in the way it respects the best

the regional particularities. At global scale, however, such data is missing for the most of the

countries. Therefore in the global database sub-country national level was used only for small

group of large countries respecting the actually available published information. Countries

and assumed sub-country regions are listed in table (Tab. 4.5).

The table of starting and end planting/harvesting periods compiled from available

sources was then used for calculation of planting and harvesting dates so that they represented

middle of reported planting or harvesting period. Length of vegetation period was calculated

as difference (in days) of harvesting and planting day and used for final refinements of

planting or harvesting dates in case calculated vegetation length value seriously exceeded

published data on typical lengths of vegetation periods given in table (Tab. 4.6).

Page 28: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

28

Tab. 4.5: Countries and assumed sub-country regions for crop calendar data specification

(regions according to crop calendar data source used for the country).

COUNTRY SUB-COUNTRY REGION

Australia New South Wales

Australia Northern Territory

Australia Queensland

Australia South Australia

Australia Victoria

Australia Western Australia

Brazil centre-south

Brazil north

Brazil north-east

Brazil south

Canada Canadian Prairies

Canada Ontario and Quebec

China centre

China north

China south

Russian Federation Central

Russian Federation Siberia

Russian Federation South

Russian Federation Ural

Because of lack of relevant global scale data on agricultural management operations it

was set arbitrary based on planting and harvesting dates as follows:

One tillage operation (ploughing) has been set for all crops cultivated in all management

systems. The plughing date has been set ten days before planting;

Manure application has been set for all crops cultivated in high input, irrigated, and low

input systems one day before ploughing;

Nitrogen application has been assumed as it is done on two separate dates for crops

cultivated in high input, irrigated and low input management systems. The first

application is done before planting and the date is set three days before ploughing. One

half of calculated nitrogen rate (chapter 4.3.4) is applied during the first application. The

second application is done during the vegetation. The date of second nitrogen application

has been for all annual crops set to half of vegetation period, for perennial crops the date

has been set to the first year of the cultivation so that the nitrogen is applied in the middle

of the period defined by planting date and end of the first year of cultivation. One half of

calculated nitrogen rate (chapter 4.3.4) is applied during the second application. Total

calculated nitrogen rate (chapter 4.3.4) is applied for selected perennial crops (cassava,

sugar cane, banana, and plantains) in the second or third year of cultivation on the middle

of the period between start of the second or third (cassava) cultivation year and date of

harvest (cassava) or first harvest (sugar cane, banana, and plantain);

Slightly modified nitrogen application scheme from that above has been set for perennial

crops from table (Tab. 4.2) with exception of the sugar cane, banana, and plantains. First

year nitrogen application is done the same way as for all other crops. In the second and

Page 29: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

29

each other years of cultivation the nitrogen applied in two separate dates so that the first

nitrogen application date is 121 days before the harvest date and the second 121 days after

the harvest date. The dates of nitrogen application are refined in that manner that two

nitrogen applications are to be done during one cultivation year;

Phosphorous and potassium application has been set on only one date before planting or

during vegetation period (in second and each other years for perennial crops from Tab 4.2.

and cassava) together with nitrogen application;

Due to lack of consistent data on irrigation water amounts on global scale the irrigation

dates for crops cultivated in irrigated management system were not set in the standing

version of global database.

Tab. 4.6: Typical lengths of vegetation period (in days) for most of global database relevant

crops (minimum, maximum and average lengths) as published by IFA 1992 and Jurášek 1997,

1998.

CROP IFA, 1992

Jurášek 1997, 1998

MIN-MAX AVERAGE MIN-MAX AVERAGE

Rice 70 - 160 115 90 - 210 150

Maize 60 - 280 170 90 - 150 120

Wheat 180 - 270 225 120 - 210 165

Barley 0 0 90 - 120 105

Sorghum 105 - 270 188 90 - 210 150

Millet 75 - 300 188 60 - 90 75

Potato 0 0 90 - 210 150

Sweet potato 120 - 180 150 120 - 180 150

Cassava 300 - 365 332,5 365 - 730 548

Yams 210 - 405 308 0 0

Bean 180 - 285 233 0 0

Pea 165 - 255 210 0 0

Chickpea 75 - 180 128 0 0

Lentil 90 - 165 128 0 0

Soybean 90 - 150 120 80 - 160 120

Groundnut 120 - 145 133 90 - 150 120

Sunflower 120 - 160 140 90 - 150 120

Oilseed Rape 320 320 270 270

Safflower 120 120 0 0

Olive_firt harvest 3 y. 3 y. 4 - 12 y. 8

Coconut_first harvest

harvest*

4,5 y. 4 1/2 y. 6 - 9 y. 7 1/2

Oilpalm 2 1/2 - 3 1/2 3 3 - 4 y. 3 1/2

Sugarcane 300 - 720 510 300 - 690 495

Sugar beet 0 0 180 - 240 210

Cotton 140 - 210 175 160 - 220 190

Flax 90 - 120 105 0 0

Jute 90 - 150 120 90 - 150 120

Sisal_first harvest 900 900 0 0

Coffee_first harvest 720 - 1080 900 4* 4*

Banana 210 - 450 220 270 - 365 318

Page 30: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

30

4.4.4. Fertlization rates

Fertilization rates data were treated separately for manure application (and nitrogen

rate coming from manure application) and N,P, and K fertilizers application.

High resolution data on nitrogen rates coming from manure application were averaged

over the SimU delineation by zonal statistics algorithm and yielded value was treated so that it

did not exceed arbitrary set maximum value of 300 kg/ha of nitrogen. Consecutively, a

coefficient 0.002 reported by (Williams et al. 1995) as default value of nitrogen amount

coming from manure was used for calculation of total manure application from manure data.

For countries covered by IFA statistics the country and crop specific information on

fertilization rates (kg/ha) of nitrogen (N), phosphorous (P2O5), and potassium (K2O) were

taken directly for high input and irrigated management systems, for low input system the

application rate was decreased to one half of total application rate reported in IFA. No

fertilization is aasumed for subsistence management system.

For other countries not covered by IFA statistics (Fig. 4.1) as well as for all the crops

of IFA countries which are not covered by the statistics FAOSTAT country-level data on total

fertilizer consumption (1000t) was recalculated to crop not-specific application rates (kg/ha)

by simple algorithm taking into account particular management system.

Fig. 4.1: Countries covered and not covered by IFA fertilizer application rates statistics.

Country specific high input and irrigated management systems areas were summed

and their area portion calculated from total cropland area. The same was done for low input

management system. Yielded area portions were used for allocation of total fertilizer

application amounts to management systems. Low input management system area portion

decreased to one half was used as coefficient for allocation total fertilizers amount (1000t)

applied in low input system in particular country, the rest of total fertilizer application

amounts was allocated to high input and irrigated management systems. Final value of

fertilizers application rate for a particular country (kg/ha) was then calculated as total

Page 31: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

31

allocated amount divided by the corresponding management systems areas. The origin of

fertilization rate data has been flagged in the metadata fields of fertilization rate table.

Consecutively, calculated rates were refined so that it fit arbitrary set maximum values

of 350 kg/ha or 175 kg/ha of N for high input and irrigated or low input management systems,

respectively. Similarly a maximum rates of P2O5 and K2O application rates were set to 300

kg/ha or 150 kg/ha for high input and irrigated or low input management systems,

respectively.

Additional rules were applied for all pulses including soybean and groundnuts and

flagged in the table so that nitrogen is applied only before harvest and maximum nitrogen

application rate has been set to 90 kg/ha or 45 kg/ha for high input and irrigated management

system or low input management system, respectively. For all management systems in the

country having no corresponding area relevant for simulation, fertilizer application rate was

arbitrary set to not-relevant (-999).

4.5. Weather data

Those EPIC model required weather parameters available from Tyndall dataset were

directly taken and included in global database. Some other weather parameters were

interpreted based on available data. Number of wet days was calculated by simple quadratic

equation Wet_days = *sqrt(total_precipitation), where was estimated for each grid cell

with the precipitation and the number of wet days in the past (Schmid et al. 2006). The

radiation was estimated from cloudiness data by the method described by Rivington et al.

(2002) where the radiation is treated as a function of day length, latitude, atmospheric

transmissivity, solar declination and solar elevation. Original as well as calculated parameters

for the past were calculated as averages of different time intervals (10, 25, 50 and 100 years)

for each climatic variable.

The variation data and probability values (standard deviation of minimal, maximal

temperature and precipitation, skewness of precipitation, number of rainy days, probabilities

of wet day after wet day and wet day after dry day) were calculated from daily sequences of

ECWMF weather data. The probabilities of wet after wet and wet after dry day were

calculated as follows: a number of wet-wet or dry-wet sequences together with dry and wet

days was calculated for each month. Consecutively, the ratio of wet-wet vs. wet and dry-wet

vs. dry was calculated. If the number of wet (or dry) days was effectively zero, a missing

value was returned. Alternatively a non-informative Bayesian correction may be used so that

for example PW|W = (wet-wet + 1)/(wet + 2).

Page 32: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

32

5. Global database data structure

GEO-BENE global database for bio-physical modeling v. 1.0 is organized in four

individual thematic datasets and one spatial reference dataset for geographical representation

and visualization of thematic data:

Spatial reference dataset (chapter 5.1);

Land cover and Land use dataset (chapter 5.2);

Topography and Soil dataset (chapter 5.3);

Cropland management dataset (chapter 5.4);

Climate dataset (chapter 5.5)

5.1. Spatial reference database (SpatialReference_v10)

Purpose of spatial reference dataset and domain tables is to reference and visualize

thematic data of global database. Database is implemented in MS Office Access 2003

database. All tables and attribute fields contains metadata descriptions. One data table, four

domain tables and one select query are included in the SpatialReference_v10 database:

SpatialReference data table, 5' spatial resolution grid centroid lattice, spatial reference for

display global database content, includes all attributes necessary for SimU and

HRU*PX30 zone delineation and relevant land cover/land use class visualization masks;

(Tab. 5.1);

D_HRUalti domain table, list and description of altitude classes used for HRU delineation

(Tab. 5.2);

D_HRUslp domain table, list and description of slope classes used for HRU delineation

(Tab. 5.3);

D_HRUsoilgrp domain table, list and description of soil classes used for HRU delineation

(Tab. 5.4);

D_COUNTRY domain table, list of countries used for global database, country

identification numbers, country names and acronyms according to United Nations

Statistics Division (Tab. 5.5);

D_ColRow30 domain table, split col_row30 identification into two separate fields (col30,

row30), only for export data to EPIC (Tab. 5.6);

SpatialReference(DataPreview&EXPORT) select query set on SpatialReference data

table, helps with the spatial reference data preview and export the data, use of select query

is optional (SQL code of query is given in APPENDIX 3);

Tab. 5.1: SpatialReference data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

Col_Row Text(50) identification of 5' spatial resolution pixel (column and row

number), counted from upper left corner;

XCOORD Number(Double) 5' spatial resolution pixel centroid x coordinate (decdeg), can be

used for data visualisation;

YCOORD Number(Double) 5' spatial resolution pixel centroid y coordinate (decdeg), can be

used for data visualisation;

Page 33: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

33

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

PxArea Number(Double) real area (ha) of 5' spatial resolution pixel;

HRU Number(Integer) homogenous response unit (HRU) code, assembled of the code of

AltiClass (first character), SlpClass (second character) and

SoilClass (third character), 88 - soil class = 88 (no-soil prevails);

Col_Row30 Text(50) identification of 30' spatial resolution pixel (column and row

number), counted from upper left corner;

COUNTRY Number(Integer) country code, numerical code by United Nations Statistics

Division;

ZoneID Number(LongInteger) HRU*PX30 zone number, can be used for display HRU*PX30

zone related data geographicaly via spatial reference (topography

and soil data);

SimUID Number(LongInteger) SimU delineation (HRU*PX30*COUNTRY) zone number, can be

used for display SimU delineation related data via spatial reference

(land cover/land use data, crop management data);

mCrpLnd Number(Byte) cropland relevant data visualisation mask, (1 = data is visualized, 0

= data is not visualized), based on arbitrary set rules;

mCrpLnd_H Number(Byte) cropland (high input management system) relevant data

visualisation mask, (1 = data is visualized, 0 = data is not

visualized), based on arbitrary set rules;

mCrpLnd_L Number(Byte) cropland (low input management system) relevant data

visualisation mask, (1 = data is visualized, 0 = data is not

visualized), based on arbitrary set rules;

mCrpLnd_I Number(Byte) cropland (irrigated high input management system) relevant data

visualisation mask, (1 = data is visualized, 0 = data is not

visualized), based on arbitrary set rules;

mCrpLnd_S Number(Byte) cropland (subsistence management system) relevant data

visualisation mask, (1 = data is visualized, 0 = data is not

visualized), based on arbitrary set rules;

mOthAgri Number(Byte) other agricultural land relevant data visualisation mask, (1 = data is

visualized, 0 = data is not visualized), based on arbitrary set rules;

mGrass Number(Byte) grassland relevant data visualisation mask, (1 = data is visualized,

0 = data is not visualized), based on arbitrary set rules;

mForest Number(Byte) forest land relevant data visualisation mask, (1 = data is visualized,

0 = data is not visualized), based on arbitrary set rules;

mWetLnd Number(Byte) wettland relevant data visualisation mask, (1 = data is visualized, 0

= data is not visualized), based on arbitrary set rules;

mOthNatLnd Number(Byte) other natural land relevant data visualisation mask, (1 = data is

visualized, 0 = data is not visualized), based on arbitrary set rules;

mNotRel Number(Byte) not-relevant land cover class relevant data visualisation mask, (1 =

data is visualized, 0 = data is not visualized), based on arbitrary set

rules;

Tab. 5.2: D_HRUalti domain table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

AltiClass Number(Byte) altitude class for HRU delineation;

Altitude Text(50) altitude (m) interval;

Page 34: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

34

Tab. 5.3: D_HRUslp domain table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

SlpClass Number(Byte) slope class for HRU delineation;

Slope Text(50) slope (deg) interval;

RepSlp Number(Byte) slope class representative value (deg);

RepSlpPerc Number(Byte) slope class representative value (%) calculated by equation

RepSlpPerc = tan(RepSlp*(3,14159/180)*100;

Tab. 5.4: D_HRUsoilgrp domain table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

SoilClass Number(Byte) slope class for HRU delineation;

Soil Text(50) soil texture, stoniness and classification by FAO/UNESCO 1974

(Soil map of the world - legend);

Tab. 5.5: D_COUNTRY domain table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

COUNTRY Number(Integer) country code, numerical code by United Nations Statistics

Division;

UN_Name Text(250) country name by United Nations Statistics Division;

UN_Acro Text(4) acronym of country by United Nations Statistics Division;

Tab. 5.6: D_ColRow30 domain table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

Col_Row30 Text(50) identification of 30' spatial resolution pixel (column, row), counted

from upper left corner;

Col30 Number(Integer) 30' spatial resolution grid column number;

Row30 Number(Integer) 30' spatial resolution grid row number;

5.2. Land cover and Land use database (LandCover&LandUse_v10)

Purpose of the dataset is to equip the bio-physical and optimization modeling with

spatial and statistical data on relevant land covers and particular land uses of arable land.

Database is implemented in MS Office Access 2003 database. All tables and attribute fields

contains metadata descriptions. Two data table, five domain tables and one select query are

included in the LandCover&LandUse_v10 database:

PixelStatistics data table, global grid pixel specific statistics on land cover/land use (Tab.

5.7);

SimUstatistics data table, SimU delineation (HRU*PX30*COUNTRY zone) aggregated

statistics on land cover/land use, SimU delineations having HRU = 88 are excluded (Tab.

5.8);

Page 35: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

35

D_HRUalti domain table, list and description of altitude classes used for HRU delineation

(Tab. 5.2, chapter 5.1);

D_HRUslp domain table, list and description of slope classes used for HRU delineation

(Tab. 5.3, chapter 5.1);

D_HRUsoilgrp domain table, list and description of soil classes used for HRU delineation

(Tab. 5.4, chapter 5.1);

D_COUNTRY domain table, list of countries used for global database, country

identification numbers, country names and acronyms according to United Nations

Statistics Division (Tab. 5.5, chapter 5.1);

D_ColRow30 domain table, split col_row30 identification into two separate fields (col30,

row30), only for export data to EPIC (Tab. 5.6, chapter 5.1);

SimUstatistics(DataPreview&EXPORT) select query set on SimUstatistics and

D_ColRow30 tables, helps with the SimU statistics preview and export the data, use of

select query is optional (SQL code of query is given in APPENDIX 3);

Tab. 5.7: PixelStatistics data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

Col_Row Text(50) identification of 5' spatial resolution pixel (column and row

number), counted from upper left corner;

HRU Number(Integer) homogenous response unit (HRU) code, assembled of the code of

AltiClass (first character), SlpClass (second character) and

SoilClass (third character), 88 - soil class = 88 (no-soil prevails);

Col_Row30 Text(50) identification of 30' spatial resolution pixel (column and row

number), counted from upper left corner;

COUNTRY Number(Integer) country code, numerical code by United Nations Statistics

Division;

SimUID Number(LongInteger) SimU delineation (HRU*PX30*COUNTRY) zone number, can be

used for display SimU delineation related data via spatial reference

(land cover/land use data, crop management data);

PxArea Number(Double) real area (ha) of 5' spatial resolution pixel;

CrpLnd Number(Double) cropland, total, physical area (ha);

CrpLnd_H Number(Double) cropland, high input management system, physical area (ha);

CrpLnd_L Number(Double) cropland, low input management system, physical area (ha);

CrpLnd_I Number(Double) cropland, irrigated high input management system, physical area

(ha);

CrpLnd_S Number(Double) cropland, subsistence management system, physical area (ha);

OthAgri Number(Double) other agricultural land (other than cropland), physical area (ha);

Grass Number(Double) grassland, physical area (ha);

Forest Number(Double) forest, physical area (ha);

WetLnd Number(Double) wetland, physical area (ha);

OthNatLnd Number(Double) other natural land, physical area (ha);

NotRel Number(Double) not relevant land, physical area (ha);

Flag Number(Byte) metadata field, identification of method used for land cover data

narmonization in particular case: 1 (CrpLnd > PxArea), 2 (CrpLnd

> total agricultural area), 3 (CrpLnd < total agricultural area), 4

(CrpLn = 0);

Page 36: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

36

Tab. 5.8: SimUstatistics data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

SimUID Number(LongInteger) SimU delineation (HRU*PX30*COUNTRY) zone number, can be

used for display SimU delineation related data via spatial reference

(land cover/land use data, crop management data);

HRU Number(Integer) homogenous response unit (HRU) code, assembled of the code of

AltiClass (first character), SlpClass (second character) and

SoilClass (third character), 88 - soil class = 88 (no-soil prevails);

Col_Row30 Text(50) identification of 30' spatial resolution pixel (column and row

number), counted from upper left corner;

COUNTRY Number(Integer) country code, numerical code by United Nations Statistics

Division;

SimUArea Number(Double) real area (ha) of SimU delineation;

CrpLnd Number(Double) cropland, total, physical area (ha);

CrpLnd_H Number(Double) cropland, high input management system, physical area (ha);

CrpLnd_L Number(Double) cropland, low input management system, physical area (ha);

CrpLnd_I Number(Double) cropland, irrigated high input management system, physical area

(ha);

CrpLnd_S Number(Double) cropland, subsistence management system, physical area (ha);

OthAgri Number(Double) other agricultural land (other than cropland), physical area (ha);

Grass Number(Double) grassland, physical area (ha);

Forest Number(Double) forest, physical area (ha);

WetLnd Number(Double) wetland, physical area (ha);

OthNatLnd Number(Double) other natural land, physical area (ha);

NotRel Number(Double) not relevant land, physical area (ha);

mCrpLnd Number(Byte) cropland relevance for SimU delineation zone (1 = relevant, 0 =

not relevant), relevant if CrpLnd/SimUArea > 0.05;

mCrpLnd_H Number(Byte) cropland (high input management system) relevance for SimU

delineation zone (1 = relevant, 0 = not relevant), relevant if

CrpLnd_H/SimUArea > 0.05;

mCrpLnd_L Number(Byte) cropland (lowinput management system) relevance for SimU

delineation zone (1 = relevant, 0 = not relevant), relevant if

CrpLnd_L/SimUArea > 0.05;

mCrpLnd_I Number(Byte) cropland (irrigated high input management system) relevance for

SimU delineation zone (1 = relevant, 0 = not relevant), relevant if

CrpLnd_I/SimUArea > 0.05;

mCrpLnd_S Number(Byte) cropland (subsistence management system) relevance for SimU

delineation zone (1 = relevant, 0 = not relevant), relevant if

CrpLnd_S/SimUArea > 0.05;

mOthAgri Number(Byte) other agricultural land relevance for SimU delineation zone (1 =

relevant, 0 = not relevant), relevant if OthAgri/SimUArea > 0.05;

mGrass Number(Byte) grassland relevance for SimU delineation zone (1 = relevant, 0 =

not relevant), relevant if Grass/SimUArea > 0.05;

mForest Number(Byte) forest land relevance for SimU delineation zone (1 = relevant, 0 =

not relevant), relevant if Forest/SimUArea > 0.05;

mWetLnd Number(Byte) wetland relevance for SimU delineation zone (1 = relevant, 0 = not

relevant), relevant if WetLnd/SimUArea > 0.05;

mOthNatLnd Number(Byte) other natural land relevance for SimU delineation zone (1 =

relevant, 0 = not relevant), relevant if OthNatLnd/SimUArea >

0.05;

mNotRel Number(Byte) not-relevant land cover classes relevance for SimU delineation

zone (1 = relevant, 0 = not relevant), relevant if NotRel/SimUArea

> 0.05;

Page 37: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

37

5.3. Topography and soil database (Topography&Soil_v10)

Purpose of the dataset is to provide EPIC model with mandatory data on topography

(geographical position, altitude and slope) and soil (soil profile and soil layer related data on

soil properties). Database is implemented in MS Office Access 2003 database. All tables and

attribute fields contains metadata descriptions. Four data table, five domain tables and two

select queries are included in the Topography&Soil_v10 database:

ZoneData data table, HRU*PX30 zone specific data on geographic position, altitude,

slope and the digital map of the world dominat soil typological unit (Tab. 5.9);

SoilAnal_MineralSoil data table, the digital soil map of the world soil typological unit

specific soil analytical data for 5 soil depth intervals (0.0 - 0.2m, 0.2 - 0.4m, 0.4 - 0.6m,

0.8 - 1.0m), mineral soils (Tab. 5.10);

SoilAnal_OrganicSoil data table, the digital soil map of the world soil typological unit

specific soil analytical data for 5 soil depth intervals (0.0 - 0.2m, 0.2 - 0.4m, 0.4 - 0.6m,

0.8 - 1.0m), organic soils (Tab. 5.11);

ClimateReference data table, assignes historical climate data pixel (5' spatial resolution) to

HRU*PX30 zone by Col_Row30 identification (Tab. 5.12);

D_HRUalti domain table, list and description of altitude classes used for HRU delineation

(Tab. 5.2, chapter 5.1);

D_HRUslp domain table, list and description of slope classes used for HRU delineation

(Tab. 5.3, chapter 5.1);

D_HRUsoilgrp domain table, list and description of soil classes used for HRU delineation

(Tab. 5.4, chapter 5.1);

D_COUNTRY domain table, list of countries used for global database, country

identification numbers, country names and acronyms according to United Nations

Statistics Division (Tab. 5.5, chapter 5.1);

D_ColRow30 domain table, split col_row30 identification into two separate fields (col30,

row30), only for export data to EPIC (Tab. 5.6, chapter 5.1);

Topo&Soil_MineralSoil(DataPreview&EXPORT) select query set on ZoneData,

SoilAnal_MineralSoil, ClimateReference and D_ColRow30 tables, helps with the

topo&soil data preview and export, use of select query is optional (SQL code of query is

given in APPENDIX 3);

Topo&Soil_OrganicSoil(DataPreview&EXPORT) select query set on ZoneData,

SoilAnal_OrganicSoil, ClimateReference and D_ColRow30 tables, helps with the

topo&soil data preview and export, use of select query is optional (SQL code of query is

given in APPENDIX 3);

Tab. 5.9: ZoneData data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

ZoneID Number(LongInteger) HRU*PX30 zone number, can be used for display HRU*PX30

zone related data geographicaly via spatial reference (topography

and soil data);

HRU Number(Integer) homogenous response unit (HRU) code, assembled of the code of

AltiClass (first character), SlpClass (second character) and

SoilClass (third character), 88 - soil class = 88 (no-soil prevails);

Col_Row30 Text(50) identification of 30' spatial resolution pixel (column and row

number), counted from upper left corner;

Page 38: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

38

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

ZoneLon Number(Double) HRU*PX30 zone specific longitude (decimal degrees), x

coordinate of 30' spatial resolution grid pixel centroid;

ZoneLat Number(Double) HRU*PX30 zone specific latitude (decimal degrees), y coordinate

of 30' spatial resolution grid pixel centroid;

ZoneArea Number(Double) HRU*PX30 zone real area (ha);

ZoneAlti Number(LongInteger) HRU*PX30 zone mean altitude(m);

ZoneSlp Number(Byte) HRU*PX30 zone representative slope (%), derived as the mean

value of the HRU slope class interval;

ZoneSTU Text(3) HRU*PX30 zone dominant digital map of the world soil

typological unit, soil unit code according to FAO map of soils of

the world legened (FAO 1974), no-soils (RK1, RK2, ST, NA, DS)

according to WISE (Batjes 2006);

Tab. 5.10: SoilAnal_MineralSoil data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

ZoneSTU Text(3) HRU*PX30 zone dominant digital map of the world soil

typological unit, soil unit code according to FAO map of soils of

the world legened (FAO 1974), no-soils (RK1, RK2, ST, NA, DS)

according to WISE (Batjes 2006);

HG Number(Byte) soil hydrological group by USDA NRCS technical manual, 1 = A,

2 = B, 3 = C, 4 = D;

ALB Number(Single) albedo of moist soil surface; arbitrary set constant (albedo = 0,15);

DEPTH1 Number(Single) depth of lower boundary of 1. soil layer (m), 0.2;

DEPTH2 Number(Single) depth of lower boundary of 2. soil layer (m), 0.4;

DEPTH3 Number(Single) depth of lower boundary of 3. soil layer (m), 0.6;

DEPTH4 Number(Single) depth of lower boundary of 4. soil layer (m), 0.8;

DEPTH5 Number(Single) depth of lower boundary of 5. soil layer (m), 1.0;

VS1 Number(Integer) volume of stones (%), content of soil fragments > 2mm; WISE

(Batjes 2006) value;

VS2 Number(Integer) as above;

VS3 Number(Integer) as above;

VS4 Number(Integer) as above;

VS5 Number(Integer) as above;

SAND1 Number(Integer) sand content (%), WISE (Batjes 2006) value;

SAND2 Number(Integer) as above;

SAND3 Number(Integer) as above;

SAND4 Number(Integer) as above;

SAND5 Number(Integer) as above;

SILT1 Number(Integer) silt content (%), WISE (Batjes 2006) value;

SILT2 Number(Integer) as above;

SILT3 Number(Integer) as above;

SILT4 Number(Integer) as above;

SILT5 Number(Integer) as above;

CLAY1 Number(Integer) clay content (%), WISE (Batjes 2006) value;

CLAY2 Number(Integer) as above;

CLAY3 Number(Integer) as above;

CLAY4 Number(Integer) as above;

Page 39: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

39

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

CLAY5 Number(Integer) as above;

BD1 Number(Single) bulk density (t/m3), WISE (Batjes 2006) value;

BD2 Number(Single) as above;

BD3 Number(Single) as above;

BD4 Number(Single) as above;

BD5 Number(Single) as above;

CEC1 Number(Single) cation exchange capacity (cmol/kg), WISE (Batjes 2006) value;

CEC2 Number(Single) as above;

CEC3 Number(Single) as above;

CEC4 Number(Single) as above;

CEC5 Number(Single) as above;

SB1 Number(Single) sum of bases (cmol/kg), WISE (Batjes 2006) derived value by

equation SB = WISE base saturation/100)* WISE CEC;

SB2 Number(Single) as above;

SB3 Number(Single) as above;

SB4 Number(Single) as above;

SB5 Number(Single) as above;

PH1 Number(Single) pH in H2O, WISE (Batjes 2006) value;

PH2 Number(Single) as above;

PH3 Number(Single) as above;

PH4 Number(Single) as above;

PH5 Number(Single) as above;

CARB1 Number(Single) (calcium) carbonate content (%), WISE (Batjes 2006) derived

value by equation CARB = WISE total carbonate

equivalent/1000g)*100;

CARB2 Number(Single) as above;

CARB3 Number(Single) as above;

CARB4 Number(Single) as above;

CARB5 Number(Single) as above;

CORG1 Number(Single) soil organic carbon content, WISE (Batjes 2006) derived value by

equation CORG = WISE organic carbon content/1000g)*100;

CORG2 Number(Single) as above;

CORG3 Number(Single) as above;

CORG4 Number(Single) as above;

CORG5 Number(Single) as above;

FWC1 Number(Double) water content in soil (mm/mm) at field water capacity, WISE

(Batjes 2006) sand,silt,clay and BD derived value (Rosetta model

and Van Genuchten equation);

FWC2 Number(Double) as above;

FWC3 Number(Double) as above;

FWC4 Number(Double) as above;

FWC5 Number(Double) as above;

WP1 Number(Double) water content in soil (mm/mm) at wilting point, WISE (Batjes

2006) sand,silt,clay and BD derived value (Rosetta model and Van

Genuchten equation);

WP2 Number(Double) as above;

WP3 Number(Double) as above;

WP4 Number(Double) as above;

Page 40: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

40

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

WP5 Number(Double) as above;

KS1 Number(Double) saturated hydraulic conductivity (mm/hour) of saturated soil, WISE

(Batjes 2006) sand,silt,clay and BD derived value (Rosetta model);

KS2 Number(Double) as above;

KS3 Number(Double) as above;

KS4 Number(Double) as above;

KS5 Number(Double) as above;

Tab. 5.11: SoilAnal_OrganicSoil data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

ZoneSTU Text(3) HRU*PX30 zone dominant digital map of the world soil

typological unit, soil unit code according to FAO map of soils of

the world legened (FAO 1974), no-soils (RK1, RK2, ST, NA, DS)

according to WISE (Batjes 2006);

HG Number(Byte) soil hydrological group by USDA NRCS technical manual, 1 = A,

2 = B, 3 = C, 4 = D;

ALB Number(Single) albedo of moist soil surface; arbitrary set constant (albedo = 0,15);

DEPTH1 Number(Single) depth of lower boundary of 1. soil layer (m), 0.2;

DEPTH2 Number(Single) depth of lower boundary of 2. soil layer (m), 0.4;

DEPTH3 Number(Single) depth of lower boundary of 3. soil layer (m), 0.6;

DEPTH4 Number(Single) depth of lower boundary of 4. soil layer (m), 0.8;

DEPTH5 Number(Single) depth of lower boundary of 5. soil layer (m), 1.0;

VS1 Number(Integer) volume of stones (%), arbitrary set to zero;

VS2 Number(Integer) as above;

VS3 Number(Integer) as above;

VS4 Number(Integer) as above;

VS5 Number(Integer) as above;

SAND1 Number(Integer) sand content (%), arbitrary set to 30, soil texture assumed to be

ballanced as for sand, silt and clay fractions portion;

SAND2 Number(Integer) as above;

SAND3 Number(Integer) as above;

SAND4 Number(Integer) as above;

SAND5 Number(Integer) as above;

SILT1 Number(Integer) silt content (%), arbitrary set to 40, soil texture assumed to be

ballanced as for sand, silt and clay fractions portion;

SILT2 Number(Integer) as above;

SILT3 Number(Integer) as above;

SILT4 Number(Integer) as above;

SILT5 Number(Integer) as above;

CLAY1 Number(Integer) clay content (%), arbitrary set to 30, soil texture assumed to be

ballanced as for sand, silt and clay fractions portion;

CLAY2 Number(Integer) as above;

CLAY3 Number(Integer) as above;

CLAY4 Number(Integer) as above;

CLAY5 Number(Integer) as above;

BD1 Number(Single) bulk density (t/m3), set to upper limit of bulk density interval for

fibric histosols given by Dreissen et al. 2001;

Page 41: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

41

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

BD2 Number(Single) as above;

BD3 Number(Single) as above;

BD4 Number(Single) as above;

BD5 Number(Single) as above;

CEC1 Number(Single) cation exchange capacity (cmol/kg); set to 95, average value

calculated from WISE database (Batjes 1995) data on soil layers of

three Od profiles from Guayana, Phillipines and New Zealand;

CEC2 Number(Single) as above;

CEC3 Number(Single) as above;

CEC4 Number(Single) as above;

CEC5 Number(Single) as above;

SB1 Number(Single) sum of bases (cmol/kg); calculated as CEC *0,25 (O, Od, Ox) or

CEC*0,75 (Oe), 0,25 is average base saturation calculated from

WISE profiles (Batjes 1995), 0.75 is median of 0,5-1 base

saturation interval defined for eutric qualifier (FAO-UNESCO

1974);

SB2 Number(Single) as above;

SB3 Number(Single) as above;

SB4 Number(Single) as above;

SB5 Number(Single) as above;

PH1 Number(Single) pH in H2O; set to 4,5 for O, Od, Ox (avg calculated from WISE

profiles, Batjes 1995) or to 6,5 for Oe (arbitrary value based on

FAO-UNESCO 1974 and Dreissen et al. 2001 characteristics of

eutric histosols);

PH2 Number(Single) as above;

PH3 Number(Single) as above;

PH4 Number(Single) as above;

PH5 Number(Single) as above;

CARB1 Number(Single) (calcium) carbonate content (%), arbitrary set to zero;

CARB2 Number(Single) as above;

CARB3 Number(Single) as above;

CARB4 Number(Single) as above;

CARB5 Number(Single) as above;

CORG1 Number(Single) organic carbon content, set to 35 based on WISE profile (Batjes

1995) average and FAO-UNESCO 1974 class definition of

histosols;

CORG2 Number(Single) as above;

CORG3 Number(Single) as above;

CORG4 Number(Single) as above;

CORG5 Number(Single) as above;

FWC1 Number(Double) water content in soil (mm/mm) at field water capacity, set to 0.52,

adopted from soil input file for european peat soil (EC6) available

in WOFOST model (version 7.1, Boogaard et al. 1998);

FWC2 Number(Double) as above;

FWC3 Number(Double) as above;

FWC4 Number(Double) as above;

FWC5 Number(Double) as above;

WP1 Number(Double) water content in soil (mm/mm) at wilting point, set to 0.13,

adopted from soil input file for european peat soil (EC6) available

in WOFOST model (version 7.1, Boogaard et al. 1998);

Page 42: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

42

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

WP2 Number(Double) as above;

WP3 Number(Double) as above;

WP4 Number(Double) as above;

WP5 Number(Double) as above;

KS1 Number(Double) saturated hydraulic conductivity (mm/hour), set to 5.62, adopted

from soil input file for european peat soil (EC6) available in

WOFOST model (version 7.1, Boogaard et al. 1998);

KS2 Number(Double) as above;

KS3 Number(Double) as above;

KS4 Number(Double) as above;

KS5 Number(Double) as above;

Tab. 5.12: ClimateReference data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

Col_Row30 Text(50) identification of 30' spatial resolution pixel (column and row

number), counted from upper left corner;

Tyndall_X Number(Double) HRU*PX30 zone relevant historical climate data (Tyndall dataset)

x-coordinate or longitude (decimal degrees) of the pixel;

Tyndall_Y Number(Double) HRU*PX30 zone relevant historical climate data (Tyndall dataset)

y-coordinate or latitude (decimal degrees) of the pixel;

Flag Number(Byte) metadata field, flag1 = 1 if no counterpart pixel to 30' spatial

resolution pixel was found in climate dataset and Tyndall_X(Y) of

the nearest pixel was used instead;

5.4. Cropland management database (CroplandManagement_v10)

Purpose of the dataset is to provide EPIC model with data on crop management

alternatives and crop shares within the particular land use, crop and crop management

calendar (dates of planting, harvesting, tillage, fertilization and irrigation) and inputs to

agriculture (fertilizer application rates). Database is implemented in MS Office Access 2003

database. All tables and attribute fields contains metadata descriptions. Seven data table, five

domain tables and three select queries are included in the CroplandManagement_v10

database:

CropCalendar data table, country and crop specific crop management operation calendar;

(Tab. 5.13);

CropShare data table, SimU and management system specific list of crops (crop share),

includes data on physical crops area and relevance masks for selection the alternatives

(Tab. 5.14);

FertilizerApplication data table, country and crop specific N, P, K fertilizer application

rates (Tab. 5.15);

ManureApplication data table, SimU delineation specific data on N comming from

manure (application rates) and total manure application (Tab. 5.16);

CropIdentification data table, list of original IFPRI dataset crops and crop groups and all

crop country alternatives for IFPRI data crop groups (Tab. 5.17);

Page 43: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

43

CropRotationRules data table, main crop suitability for cultivation after the preceeding

crop, rules; (Tab. 5.18);

CropDuration data table, duration of crop cultivation for perennial crops (Tab. 5.19);

D_HRUalti domain table, list and description of altitude classes used for HRU delineation

(Tab. 5.2, chapter 5.1);

D_HRUslp domain table, list and description of slope classes used for HRU delineation

(Tab. 5.3, chapter 5.1);

D_HRUsoilgrp domain table, list and description of soil classes used for HRU delineation

(Tab. 5.4, chapter 5.1);

D_COUNTRY domain table, list of countries used for global database, country

identification numbers, country names and acronyms according to United Nations

Statistics Division (Tab. 5.5, chapter 5.1);

D_ColRow30 domain table, split col_row30 identification into two separate fields (col30,

row30), only for export data to EPIC (Tab. 5.6, chapter 5.1);

D_SubCountry list of first sub-country region used for further specification of country-

level crop calendar data, original sub-country identification numbers and names according

to GAUL dataset (Tab. 5.20);

CropCalendar(DataPreview&EXPORT) select query set on CropCalendar table, helps

with the crop management calendar data preview and export, metadata descrition of

attribude field is given in design mode of table, use of select query is optiona (SQL code

of query is given in APPENDIX 3);

CropShare(DataPreview&EXPORT) select query set on CropShare and D_ColRow30

tables, helps with the crop share data preview and export, use of select query is optiona

(SQL code of query is given in APPENDIX 3);

ManureApplication(DataPreview&EXPORT) select query set on ManureApplication and

D_ColRow30 tables, helps with the manure application rates data preview and export, use

of select query is optiona (SQL code of query is given in APPENDIX 3);

Tab. 5.13: CropCalendar data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

COUNTRY Number(Integer) country code, numerical code by United Nations Statistics

Division;

CrpCalSpec Number(Byte) identification number of sub-region for further specification of

country level crop calendar;

CROP Text(4) code of original IFPRI crop and where appropriate IFPRI crop

group (SWPY, BANP, OOIL, OPUL) replaced by country most

frequent representative of the crop group;

OPER Text(50) identification of agro-technic operation;

YEA Number(Byte) year of crop cultivation;

DAY Number(Integer) number of day of agrotechnical operation (counted from 1.1. to

31.12., 29.2. not included);

DAT Date/Time date of agro technical operation (1.1. 2001 - 31.12.2005);

mHI Number(Byte) operation relevance for crop cultivated in high input management

system (1 = relevant, 0 = not-relevant);

mIR Number(Byte) operation relevance for crop cultivated in irrigated high input

management system (1 = relevant, 0 = not-relevant);

mLI Number(Byte) operation relevance for crop cultivated in low input management

system (1 = relevant, 0 = not-relevant);

mSS Number(Byte) operation relevance for crop cultivated in subsistence management

Page 44: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

44

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

system (1 = relevant, 0 = not-relevant);

Tab. 5.14: CropShare data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

SimUID Number(LongInteger) SimU delineation (HRU*PX30*COUNTRY) zone number, can be

used for display SimU delineation related data via spatial reference

(land cover/land use data, crop management data);

MngSystem Text(2) identification of management system, HI - high input, LI - low

input, IR - irrigated high input, SS - subsistence system,

management systems according to You et Wood 2006;

HRU Number(Integer) homogenous response unit (HRU) code, assembled of the code of

AltiClass (first character), SlpClass (second character) and

SoilClass (third character), 88 - soil class = 88 (no-soil prevails);

Col_Row30 Text(50) identification of 30' spatial resolution pixel (column and row

number), counted from upper left corner;

COUNTRY Number(Integer) country code, numerical code by United Nations Statistics

Division;

CrpCalSpec Number(Byte) identification number of sub-region for further specification of

country level crop calendar;

CROP Text(4) code of original IFPRI crop and where appropriate IFPRI crop

group (SWPY, BANP, OOIL, OPUL) replaced by country most

frequent representative of the crop group;

CultType Number(Byte) crop cultivation type, crop classification according to duration of

crop cultivation on the field, 1 (crops cultivated for < 365 days), 2

(crops cultivated for > 365 days);

RotType Number(Byte) crop rotation type, 1 (crops cultivated in monoculture), 2 (crops

cultivated in rotation);

CrpArea Number(Double) SimU delineation specific physical area (ha) of particular crop;

SimUmask Number(Byte) SimU relevance mask (1 = relevant, 0 = not relevant), relevant if

particular management system fulfills the area portion threshold

(taken from land cover/land use statistic dataset);

absSimUmask Number(Byte) absolute SimU relevance mask (1 = relevant, 0 = not relevant),

relevant only for one management system having the spatial

dominance in the SimU delineation;

CropMask Number(Byte) crop relevance mask (1 = relevant, 0 = not relevant), relevant is

crop having more than 5% area portion of the particular

management system total area;

AltMask Number(Byte) alternative mask (1 = relevant, 0 = not-relevant), mark SimU

relevant management alternative (rotation or monoculture1,

monoculture2, monoculture n) which is spatially dominant in SimU

delineation;

Tab. 5.15: FertilizerApplication data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

COUNTRY Number(Integer) country code, numerical code by United Nations Statistics

Division;

CROP Text(4) code of original IFPRI crop and where appropriate IFPRI crop

group (SWPY, BANP, OOIL, OPUL) replaced by country most

frequent representative of the crop group;

N1_HiIr Number(Double) nitrogen (N) application 1 (kg/ha), before planting or during

Page 45: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

45

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

vegetation (perennial crops) together with P, K application, rainfed

and irrigated high input management system, -999 no relevant (no

area of corresponding management system in COUNTRY);

N2_HiIr Number(Double) nitrogen (N) application 2 (kg/ha), during vegetation, rainfed and

irrigated high input management system, -999 no relevant (no area

of corresponding management system in COUNTRY);

N3_HiIr Number(Double) nitrogen (N) application 3 (kg/ha), during vegetation (third year for

selected crops), rain fed and irrigated high input management

system, -999 no relevant (no area of corresponding management

system in COUNTRY);

P_HiIr Number(Double) phosphorous (P2O5) application (kg/ha), before planting or during

vegetation (perennial crops), rain fed and irrigated high input

management system, -999 no relevant (no area of corresponding

management system in COUNTRY);

K_HiIr Number(Double) potassium (K2O) application (kg/ha), before planting or during

vegetation (perennial crops), rain fed and irrigated high input

management system, -999 no relevant (no area of corresponding

management system in COUNTRY);

N1_Li Number(Double) nitrogen (N) application 1 (kg/ha), before planting or during

vegetation (perennial crops) together with P, K application, low

input management system, -999 no relevant (no area of

corresponding management system in COUNTRY);

N2_Li Number(Double) nitrogen (N) application 2 (kg/ha), during vegetation, low input

management system, -999 no relevant (no area of corresponding

management system in COUNTRY);

N3_Li Number(Double) nitrogen (N) application 3 (kg/ha), during vegetation (third year for

selected crops), low input management system, -999 no relevant

(no area of corresponding management system in COUNTRY);

P_Li Number(Double) phosphorous (P2O5) application (kg/ha), before planting or during

vegetation (perennial crops), low input management system, -999

no relevant (no area of corresponding management system in

COUNTRY);

K_Li Number(Double) potassium (K2O) application (kg/ha), before planting or during

vegetation (perennial crops), low input management system, -999

no relevant (no area of corresponding management system in

COUNTRY);

Flag1 Number(Byte) metadata field, if = 1 then data was taken directly from original

IFA country and crop specific dataset;

Flag2 Number(Byte) metadata field, if = 1 then data was calculated from FAOSTAT

dataset on country specific N, P, K consumption;

Flag3 Number(Byte) metadata field, if = 1 then max. 90 kg/ha (rain fed and irrigated

high input) or 45 kg/ha (low input management systems)

application rates were arbitrary set for pulses, soya and groundnuts

in case calculated value exceeded the threshold;

Tab. 5.16: ManureApplication data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

SimUID Number(LongInteger) SimU delineation (HRU*PX30*COUNTRY) zone number, can be

used for display SimU delineation related data via spatial reference

(land cover/land use data, crop management data);

HRU Number(Integer) homogenous response unit (HRU) code, assembled of the code of

AltiClass (first character), SlpClass (second character) and

SoilClass (third character), 88 - soil class = 88 (no-soil prevails);

Page 46: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

46

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

Col_Row30 Text(50) identification of 30' spatial resolution pixel (column and row

number), counted from upper left corner;

COUNTRY Number(Integer) country code, numerical code by United Nations Statistics

Division;

Nrate Number(Integer) nitrogen (kg/ha) coming from manure application, taken from high

resolution global scale data (Liu et al. 2008);

Manure Number(LongInteger) total manure application (kg/ha), arbitrary set as Nrate/0,002;

Tab. 5.17: CropIdentification data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

CROP Text(4) code of original IFPRI crop and where appropriate IFPRI crop

group (SWPY, BANP, OOIL, OPUL) replaced by country most

frequent representative of the crop group;

CrpName Text(50) crop name;

Flag Number(Byte) metadata field, identification of crop source, 1 = original IFPRI

data list of crops or crop groups, 2 = country specific representative

for crop group;

Tab. 5.18: CropRotationRules data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

CROP Text(4) main crop, code of original IFPRI crop and where apropriate IFPRI

crop group (SWPY, BANP, OOIL, OPUL) replaced by country

most frequent representative of the crop group;

BARL Number(Byte) preceding crop, suitability for main crop: 0 = not suitable, 1 = not

suitable but possible, 2 = suitable or very suitable;

BEAN Number(Byte) as above

CASS Number(Byte) as above

GROU Number(Byte) as above

MAIZ Number(Byte) as above

JUTE Number(Byte) as above

SUNF Number(Byte) as above

BHBE Number(Byte) as above

POTA Number(Byte) as above

RICE Number(Byte) as above

SORG Number(Byte) as above

SOYB Number(Byte) as above

SWTP Number(Byte) as above

WHEA Number(Byte) as above

SUGC Number(Byte) as above

COTT Number(Byte) as above

SUGB Number(Byte) as above

MILL Number(Byte) as above

MUST Number(Byte) as above

SESA Number(Byte) as above

SAFF Number(Byte) as above

Page 47: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

47

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

RAPE Number(Byte) as above

PEAS Number(Byte) as above

MELO Number(Byte) as above

LINS Number(Byte) as above

LENT Number(Byte) as above

FLAX Number(Byte) as above

CPEA Number(Byte) as above

YAMS Number(Byte) as above

BANA Number(Byte) as above

PLAN Number(Byte) as above

Tab. 5.19: CropDuration data table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

CROP Text(4) code of original IFPRI crop and where appropriate IFPRI crop

group (SWPY, BANP, OOIL, OPUL) replaced by country most

frequent representative of the crop group;

DURATION Number(Byte) duration (years) of crop cultivation;

LastYearRepeat Number(Byte) number of cultivation years after the last year indiced in crop

calendar, the same calendar data as for the last year;

Tab. 5.20: D_SubCountry domain table attribute fields descriptions.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

COUNTRY Number(Integer) country code, numerical code by United Nations Statistics

Division;

GAUL1_code Number(LongInteger) identification of first sub-country region, GAUL administrative

region code;

CrpCalSpec Number(Byte) identification number of sub-region for further specification of

country level crop calendar;

NameCrpCalSpec Text(50) name of subregion for further specification of country level crop

calendar;

5.5. Climate database (Climate_v10)

The data set provides the necessary climate input data (climate in the past and future climate

scenarios). The climate database consists of two separate data sets:

HistoricalClimate_v10 is a MS Office Access 2003 database where the Tyndall weather

data from 1901 to 2002 together with the monthly statistics derived from ECMWF

database are stored. Six data tables hold the data (name specification is ts_

cru_ts_2_10_1901_2002_<interval>_<variable-name>) _cld (cloud cover in %), _pre

(precipitation in mm), _rad (radiation in MJ/m²), _tmn (near-surface temperature

minimum in °C), _tmx (near-surface temperature maximum in °C), and _wet (wet day

frequency in days) separately for 10, 25, 50, and 100 year intervals. Seven tables contain

the statistical values sd_tmin (standard deviation of minimum temperature), sd_tmax

(standard deviation of maximum temperature), sd_rain (standard deviation of

Page 48: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

48

precipitation), sk_rain (skewness of precipitation), ww_rain (probabilities of wet day after

wet day) and dw_rain (probabilities of wet day after dry day). All tables have the same

structure, described in Tab. 5.21;

Eight Tyndall climate change scenarios are available as postgreSQL databases. The

database backups comprise the PCM and HadCM3 scenarios for the 4 different global

warming values A1FI, A2, B1 and B2. Each backup file contains seven tables (name

specification is: scenario_<global-climate-model>_<variable-name>): _cld (cloud cover

in %), _pre (precipitation in mm), _rad (radiation in MJ/m²), _tmn (near-surface

temperature minimum in °C), _tmx (near-surface temperature maximum in °C), _tmp

(near-surface temperature in °C), and _wet (wet day frequency in days). All tables have

the same structure, described in Tab. 5.22.

Tab. 5.21: HistoticalClimate_v10 structure of tables and identification of attribute fields.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

period Number(Integer) time interval (years) for the average value of the Tyndall dataset;

lon Number(single) longitude of 0.5° (Tyndall) or 2.5° (ECWMF) spatial resolution

pixel, pixel identification;

lat Number(single) latitude of 0.5° (Tyndall) or 2.5° (ECWMF) spatial resolution

pixel, pixel identification;

jan Number(single) mean value for January, depends on the table theme;

feb Number(single) mean value for February, depends on the table theme;

mar Number(single) mean value for March, depends on the table theme;

april Number(Single) mean value for April, depends on the table theme;

may Number(single) mean value for May, depends on the table theme;

jun Number(single) mean value for June, depends on the table theme;

jul Number(single) mean value for July, depends on the table theme;

aug Number(single) mean value for August, depends on the table theme;

sep Number(single) mean value for September, depends on the table theme;

oct Number(single) mean value for October, depends on the table theme;

nov Number(single) mean value for November, depends on the table theme;

dec Number(single) mean value for December, depends on the table theme;

Tab. 5.22: ClimateScenarios backups table structure and identification of attribute fields.

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

year Number(LongInteger) year, range 1 (2001) – 100 (2100);

lon Number(single) longitude of 0.5° spatial resolution pixel, pixel identification;

lat Number(single) latitude of 0.5° spatial resolution pixel, pixel identification;

jan Number(single) mean value for January, depends on the table theme;

feb Number(single) mean value for February, depends on the table theme;

mar Number(single) mean value for March, depends on the table theme;

april Number(Single) mean value for April, depends on the table theme;

may Number(single) mean value for May, depends on the table theme;

jun Number(single) mean value for June, depends on the table theme;

jul Number(single) mean value for July, depends on the table theme;

aug Number(single) mean value for August, depends on the table theme;

Page 49: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

49

ATTRIBUTE FIELD DATA TYPE DESCRIPTION

sep Number(single) mean value for September, depends on the table theme;

oct Number(single) mean value for October, depends on the table theme;

nov Number(single) mean value for November, depends on the table theme;

dec Number(single) mean value for December, depends on the table theme;

Page 50: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

50

References

Adams, D.M.; Alig, R.J.; Callaway, J.M.; McCarl, B.A.; Winnett, S.M., 1996. The forest and

agricultural sector optimization model (FASOM): model structure and policy applications.

Res. Pap. PNW-RP-495. Portland, OR: U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station. 60 p.

Balkovič, J., Schmid, E., Bujnovský, R., Skalský, R., Poltárska, K., 2006. Biophysical

modelling for evaluating soil carbon sequestration potentials on arable land in the pilot

area Baden-Württemberg (Germany). Agriculture, Vol. 52, No. 4, pp. 169 – 176

Batjes, N.H. (Ed.), 1995. A homogenized soil data file for globalenvironmental research: a

subset of FAO. ISRIC and NRCS profiles(Version 1.0). Working Paper and Preprint

95/10b. ISRIC - World Soil Information, Wageningen, 43 p. Available on internet:

<http://www.isric.org/ISRIC/WebDocs/Docs/isric_wp95_10.pdf>, Accessed: February 27

2008

Batjes, N., H., 2006. ISRIC-WISE derived soil properties on a 5 by 5 arc-minutes global grid

(ver. 1.1). Report 2006/2, ISRIC - World Soil Information, Wageningen, 46 pp., Available

on internet: http://www.isric.org/isric/webdocs/Docs/ISRIC_Report_2006_02.pdf,

Accessed: February 8, 2008

Batjes, N. H., Bridges, E.M., Nachtergaele, F.O., 1995. World Inventory of Soil Emission

Potentials: Development of a Global Soil Data Base of Process-Controlling Factors. In

Peng, S., Ingram, K.T., Neue, H.U., Ziska, L.H. (Eds.) 1995. Climate change and rice.

Springer-Verlar, Berlin, Heidelberg, p. 102 – 115

Boogaart, H.L., Van Diepen, C.A., Rotter, R.P., Cabrera, J.M., Van Laar, H.H., 1998: User´s

guide for the WOFOST 7.1 crop growth simulation model and WOFOST Control Center

1.5. Technical document. DLO Winand Staring Centre, Wageningen, 141 p.

Di Gregorio, A., Jansen, L., 2000. Land cover classification system, classification concepts

and user manual. FAO, Rome, Available on internet: <http://www.fao.org/

docrep/003/X0596E/X0596E00.HTM>, Accessed: February 14 2008

Dobos, E., 2006. Albedo. In Lal, R. (Ed.). Encyclopedia of Soil Science – Second edition,

Volume 1. Taylor&Franciss CRC Press, New York, (2006), pp. 63 – 65

Driessen, P., Deckers, J., Spaargaren, O., Nachtergaele, F. 2001. Lecture notes on the major

soils of the world. World Soil Resources Reports 94, FAO, Rome, 334 p.

FAO, 1959a. World crop harvest calendar for sugar beets. Meeting of Experts of Time

Reference (7 – 18 Dec.). FAO, Roma, 6 p.

FAO, 1959b. World crop harvest calendar for sugar cane. Meeting of Experts of Time

Reference (7 – 18 Dec.). FAO, Roma, 7 p.

FAO, 1959c. World crop harvest calendar for coffee. Meeting of Experts of Time Reference

(7 – 18 Dec.). FAO, Roma, 7 p.

FAO-UNESCO, 1974. Soil map of the world, Volume 1 - Legend. Unesco, Paris, 59 p.

Fischer, G., van Velthuizen, H., Nachtergele, F., 2000. Global Agro-Ecological Zones

Assessment: Methodology and Results. Interim Report IR-00-064, IIASA, Laxenburg, 48

p., Available on internet: <http://www.iiasa.ac.at/Admin/PUB/Documents/RR-02-

002.pdf>, Acessed: February 14 2008

IFA, 1992. World Fertilizer Use Manual. Germany, 1992. 632 p. ISBN 2-9506299-0-3.

Jurášek, P., 1997. World Agriculture. Part I. (in Slovak). AT Publishing, Bratislava, 263 p.

ISBN 80-967812-0-0.

Jurášek, P., 1998. World Agriculture. Part II. (in Slovak). AT Publishing, Bratislava, 387 s.

ISBN 80-967812-9-4.

Page 51: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

51

Justice C.O. and Becker-Reshef I.(Eds.) (2007) Report from the Workshop on Developing a

Strategy for Global Agricultural Monitoring in the framework of Group on Earth

Observations (GEO), UN FAO, July 2007, Geography Dept., University of Maryland, 66,

pp, Available on internet: <ftp://ftp.iluci.org/GEO_Ag> , Accessed: February 6, 2008

Kučera, L., Genovese, G., (Eds.), 2004a. Crop Monographies on Central European Countries.

Vol. 2 Estonia, Latvia, Lithuania, Poland. Office for Official Publications of the European

Communities, Luxembourg, 122 pp. ISBN 92-894-8177-3. Available on internet:

(http://agrifish.jrc.it/marsstat/Crop_Yield_Forecasting/MOCA/00000000.HTM)

Accessed: March 11, 2008.

Kučera, L., Genovese, G., (Eds.), 2004b. Crop Monographies on Central European Countries.

Vol. 3 Czech Republic, Slovakia, Hungary, Slovenia. Office for Official Publications of

the European Communities, Luxembourg, 160 pp. ISBN 92-894-8178-1. Available on

internet: (http://agrifish.jrc.it/marsstat/Crop_Yield_Forecasting/MOCA/00000000.HTM)

Accessed: March 11, 2008.

Kučera, L., Genovese, G., (Eds.), 2004c. Crop Monographies on Central European Countries.

Vol. 4 Romania, Bulgaria, Turkey. Office for Official Publications of the European

Communities, Luxembourg, 122 pp. ISBN 92-894-8179-X. Available on internet:

(http://agrifish.jrc.it/marsstat/Crop_Yield_Forecasting/MOCA/00000000.HTM )

Accessed: March 11, 2008.

Lethcoe, K.J., Klaver, R.W., 1998. Simulating the Interrupted Goode Homolosine Projection

With ArcInfo. 18th Annual ESRI International User Conference in San Diego

Proceedings, California, Available on internet: <http://gis.esri.com/library/userconf/

proc98/PROCEED.HTM>, Accessed: February 12, 2008

Liu J., You L., Obersteiner M., Zehnder A.J.B. and Yang, H., 2008. A high-resolution

assessment on global nitrogen flows in cropland. In preparation.

MacKerron, D. K. L., 1992. Agrometeorological Aspects of Forecasting Yields of Potato

within the E. C. Office for Official Publications of the European Communities,

Luxembourg, 250 pp. ISBN 92-826-3538-4.

Mitchell, T. D., Carter, T. R., Jones, P. D., Hulme, M., New, M., 2004. A comprehensive set

of high-resolution grids of monthly climate for Europe and the globe: the observed record

(1901-2000) and 16 scenarios (2001-2100). Tyndall Centre for Climate Change Research

Working Paper 55. 25 p., Available on Internet: http://www.tyndall.ac.uk/publications/

working_papers/wp55.pdf, Accessed: March 14, 2008

Narciso, G., Ragni, P., Venturi, A., 1992. Agro-meteorological aspects of crops in Italy, Spain

and Greece: A summary review for common and durum wheat, barley, maize, rice, sugar

beet, sunflower, soya bean, rape, potato, tobacco, cotton, olive and grape crops. Office for

Official Publications of the European Communities, Luxembourg, 440 pp. ISBN 92-826-

3995-6.

Rivington, M., Matthews, K.B., Buchan, K., 2002. A Comparison of Methods for Providing

Solar Radiation Data to Crop Models and Decision Support Systems. In Rizzoli, A.E.,

Jakeman, J.J., (Eds.) Integrated Assessment and Decision Support, Proceedings of the 1st

Biennial Meeting of the iEMSs, Volume 3, The International Environmental Modelling

and Software Society, Lugano, Switzerland, pp. 193 – 198, Available on Internet:

http://www.wcai-

infonet.org/servlet/BinaryDownloaderServlet?filename=1065620840070_wdds25.pdf&ref

ID=113918, Accessed: March 14, 2008

Rossiter, D. G. 2003. Biophysical models in land evaluation. In Encyclopedia of Life Support

Systems (EOLSS), 2003, 16 p. Available on internet: <www.itc.nl/~rossiter/

Docs/EOLSS_1527_Preprint.pdf >, Accessed: February 7, 2008

Page 52: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

52

Russell, G., 1990. Barley Knowledge Base. Office for Official Publications of the European

Communities, Luxembourg, 142 pp. ISBN 92-826-1647-9.

Russell, G., Wilson, G. W., 1994. An Agro-Pedo-Climatological Knowledge-Base of Wheat

in Europe. Office for Official Publications of the European Communities, Luxembourg,

160 pp. CL-NA-15789-EN-C.

Schmid, E., Balkovič, J., Moltchanova E., Skalský, R., Poltárska, K., Müller, B., Bujnovský,

R., 2006: Biophysical process modeling for EU25: concept, data, methods, and results.

Deliverable D3 (T30), Final Report, Appendix II., EU FP 6 Project INSEA – Integrated

Sink Enhancement Assessment (SSPI-CT-2003/503614 with DG RTD), 2006, 76 p.

Schaap, M.G., Bouten, W., 1996. Modelling Water Retention Curves of Sandy Soils Using

Neural Networks. Water Resour. Res., Vol. 32, s. 3033 – 3040.

Siebert, S., Döll, P., Feick, S., Hoogeveen, J., Frenken, K., 2007. Global Map of Irrigation

Areas version 4.0.1. Johann Wolfgang Goethe University, Frankfurt an Main, Germany/

FAO, Rome. Available on internet: (http://www.fao.org/nr/water/aquastat/

irrigationmap/index10.stm ). Accessed: March 11 2008.

Stolbovoy, V., Montanarella, L., Panagos, P., (Eds.) 2007. Carbon Sink Enhancement in Soils

of Europe: Data, Modeling, Verification. JRC technical and scientific reports, EUR 23037

EN, Office for Official Publications of the European Communities, Luxembourg, 183 p.

Available on internet: <http://eusoils.jrc.it/ESDB_Archive/eusoils_docs/other/

EUR23037.pdf>, Acessed: February 27, 2008

USDA, 1994. Major World Crop Areas and Climatic Profiles. World Agricultural Outlook

Board, U. S. Department of Agriculture. Agriculture Handbook No. 664., 279 p.

USDA-NRCS, 2007. Hydrologic Soil Groups. National Engineering Handbook : Part 630

Hydrology : Chapter 7. 210-VI-NEH-630.7, USDA, Washington DC. Available on

internet: <http://directives.sc.egov.usda.gov/media/pdf/H_210_630_7.pdf>, Accessed

February 21 2008

Williams, J.R. 1995. The EPIC Model. In Computer Models of Watershed Hydrology (Ed.:

V.P. Singh). Water Resources Publications, Highlands Ranch, Colorado, 1995, pp 909-

1000.

Wösten, J. H. M., Van Genuchten, M. T., 1988. Using Texture and Other Soil Properties to

Predict the Unsaturated Soil Hydraulic Functions. Soil. Sci. Soc. Am. J., Vol. 52, p. 1762

- 1770

You, L. and S. Wood. 2006. An entropy approach to spatial disaggregation of agricultural

production. Agricultural Systems Vol.90, Issues1-3 p.329-347.

Page 53: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

53

Appendix 1: Input data requirements of the EPIC model

Data on four major input data components: weather, soil, topography and management

practices is essential to run EPIC.

The weather variables necessary for running EPIC are precipitation (in mm),

minimum and maximum air temperature (in degree Celsius), and solar radiation (in MJ/m²). If

the Penman methods are used to estimate potential evapo-transpiration, wind speed (in m/sec

measured at 10 m height), and relative humidity (in %) are also required. If measured daily

weather data is available it can be directly input into EPIC. In addition, monthly statistics of

this daily weather (mean, standard deviation, skew coefficient, probabilities of wet-dry and

wet-wet days, etc.) need to be computed and input in the model. EPIC provides a statistical

support program to compute the statistics of relevant variables based on daily weather

records. Consequently, long historical daily weather records (20-30 years) for all weather

variables are desirable for statistical parameter calculations. Based on the weather variable

statistics, EPIC can generate weather patterns for long-run analyses (100+ years), or as

indicated above, daily weather records (e.g., from world climate models with downscaling

procedures) can be input directly. There is also the option of reading a sequence of actual

daily weather and use generated weather afterwards or before within a simulation run.

The topography of a field or HRU is described by average field size (ha), slope length

(m), and slope (%). In addition, elevation, longitude and latitude are needed for each site or

HRU. Information on elevation, longitude and latitude is usually provided by a climate station

of a digital elevation map.

A large number of physical and chemical soil parameters to describe each soil layer

and subsequently an entire soil profile can be input in EPIC. These soil parameters (Tab.1) are

separated between general and layer-specific as well as between essential and useful soil

information requirement. The essential soil parameters are mandatory input while the

remaining ones would help to further describe a soil specific profile situation. In EPIC, a soil

profile can be split in up to 10 soil layers of which each is described by a specific set of

chemical and physical soil parameters. If, for instance, the description of only two soil layers

is available (top-soil and sub-soil), EPIC still allows (optional) to split the soil profile into ten

soil layers. It assures that e.g., soil temperature and soil moisture can be appropriately

estimated through soil layers and time.

Wide range of management scheduling in EPIC allows flexibility in modelling different

cropping, forestation, and tillage systems (including crop rotations and crop mixes).

However, it requires reliable information of the actual management practice for a given region

or site. Generally, information on:

Date of planting (including potential heat units the crop needs to reach maturity);

Date, type (commercial; dairy, swine, etc. manure), and amount of fertilizer (elemental

NPK) in kg/ha; if manure is applied, information on application rate (in case of grazing

the stocking rate) and nutrient composition (orgN, minN (NO3-N + NH3-N), orgP, minP,

minK, orgC, and fraction of NH3-N on minN) is needed;

Date and amount of irrigation (including NO3 and salt concentration in irrigation water) in

mm;

Date and type of tillage operation (plough, harrow spike, field cultivator, thinning, etc.);

and

Date and type of harvesting (combine, hay cutting, grazing, etc.) is needed.

Page 54: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

54

Table 1: List of physical and chemical soil parameters needed by EPIC

ESSENTIAL SOIL INFORMATION USEFUL SOIL INFORMATION

general soil and hydrologic information

soil albedo (moist) initial soil water content (fraction of field capacity)

hydrologic soil group (A, B, C, or D) minimum depth to water table in m

maximum depth to water table in m

initial depth to water table in m

initial ground water storage in mm

maximum ground water storage in mm

ground water residence time in days

return flow fraction of water percolating through root

zone soil weathering (CaCO3 soils; non-CaCO3 soils that

are slightly, moderately or highly weathered) number of years of cultivation

soil group (kaolinitic, mixed, or smetitic)

fraction of org C in biomass pool

fraction of humus in passive pool

soil weathering code

by soil layer

depth from surface to bottom of soil layer in m bulk density of the soil layer (oven dry) in t/m3

bulk density of the soil layer (moist) in t/m3 wilting point (1500 kPa for many soils) in m/m

sand content in % field capacity (33 kPA for many soils) in m/m

silt content in % Initial organic N concentration in g/t

soil pH sum of bases in cmol/kg

organic carbon in % cation exchange capacity in cmol/kg

calcium carbonate content in % coarse fragment content in %vol.

initial soluble N concentration in g/t

initial soluble P concentration in g/t

initial organic P concentration in g/t

exchangeable K concentration in g/t

crop residue in t/ha

saturated conductivity in mm/h

fraction of storage interacting with NO3 leaching

phosphorous sorption ratio

lateral hydraulic conductivity in mm/h

electrical conductivity in mm/cm

structural litter kg/ha

metabolic litter kg/ha

lignin content of structural litter in kg/ha

carbon content of structural litter in kg/ha

C content of metabolic litter in kg/ha

C content of lignin of structural litter in kg/ha

N content of lignin of structural litter in kg/ha

C content of biomass in kg/ha

C content of slow humus in kg/ha

C content of passive humus kg/ha

N content of structural litter in kg/ha

N content of metabolic litter in kg/ha

N content of biomass in kg/ha

N content of slow humus in kg/ha

N content of passive humus in kg/ha

observed C content at the end of simulation

Page 55: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

55

Appendix 2: Selected sources of national crop calendar data

COUNTRY SOURCE

Afghanistan http://www.icarda.org/afghanistan/NA/Full/Primary_F.htm

Angola http://www.fao.org/docrep/009/j8081e/j8081e00.htm

Australia http://151.121.3.140/remote/aus_sas/crop_information/calendars/index_of_clndrs.htm

Bangladesh http://151.121.3.140/remote/aus_sas/crop_information/calendars/index_of_clndrs.htm

Burkina Faso http://v4.fews.net/docs/Publications/Burkina_200704en.pdf

Canada http://www.fas.usda.gov/remote/canada/index.htm

Côte d´Ivoire http://www.fas.usda.gov/pecad2/highlights/2002/09/West_Africa/pictures/ic_cropcalendar.

htm

Ethiopia http://www.fas.usda.gov/pecad2/highlights/2002/10/ethiopia/baseline/Eth_Crop_Calendar.

htm

India http://151.121.3.140/remote/aus_sas/crop_information/calendars/index_of_clndrs.htm

Iran http://www.fas.usda.gov/remote/mideast_pecad/iran/iran.htm

Iraq www.nationalaglawcenter.org/assets/crs/RL32093.pdf

Kazakhstan http://www.fas.usda.gov/pecad2/highlights/2005/03/Kazakh_Ag/crop_cal.htm

Kenya http://www.fas.usda.gov/pecad/highlights/2004/12/Kenya/images/crop_calendar.htm

Madagascar http://www.wildmadagascar.org/maps/crops.html

Malawi http://v4.fews.net/docs/Publications/South_200710en.pdf

Mali http://v4.fews.net/docs/Publications/Mali_200612en.pdf

Mozambique http://v4.fews.net/docs/Publications/Mozambique_200607en.pdf

Nicaragua http://usda.gov/pecad2/articles/99-03/nic0399.pdf

Nigeria www.fas.usda.gov/pecad2/highlights/2002/03/nigeria/pictures/crop_calendar_nigeria.pdf

Russian

Federation http://www.fas.usda.gov/remote/soviet/Crop_Calendars_2004/rs_districts.htm

Rwanda www.fews.net/centers/files/Rwanda_200009en.pdf

Sudan http://www.sudan.net/statistic/sudancal.html

Tanzania http://www.fas.usda.gov/pecad2/highlights/2003/03/tanzania/images/crop_calendar.htm

Turkey http://www.fas.usda.gov/remote/mideast_pecad/turkey/turkey.htm

Zambia http://v4.fews.net/docs/Publications/Zambia_200705en.pdf

Zimbabwe http://www.fas.usda.gov/pecad2/highlights/2004/06/zimbabwe/images/zimbabwe_cropcale

ndar.htm

Viet Nam www.cimmyt.org/english/docs/maize_producsys/vietnam.pdf

Uganda www.foodnet.cgiar.org/inform/Fews/fews00may.pdf

Page 56: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

56

Appendix 3: SQL codes of select queries for preview and export the data

1. SQL code of SpatialReference(DataPreview&EXPORT) select query:

SELECT SpatialReference.Col_Row, SpatialReference.XCOORD, SpatialReference.YCOORD,

SpatialReference.ZoneID, SpatialReference.SimUID, SpatialReference.mCrpLnd, SpatialReference.mCrpLnd_H,

SpatialReference.mCrpLnd_L, SpatialReference.mCrpLnd_I, SpatialReference.mCrpLnd_S,

SpatialReference.mOthAgri, SpatialReference.mGrass, SpatialReference.mForest, SpatialReference.mWetLnd,

SpatialReference.mOthNatLnd, SpatialReference.mNotRel

FROM SpatialReference;

2. SQL code of SimUstatistics(DataPreview&EXPORT) select query:

SELECT SimUstatistics.SimUID, SimUstatistics.HRU, D_ColRow30.Col30, D_ColRow30.Row30,

SimUstatistics.COUNTRY, SimUstatistics.SimUArea, SimUstatistics.CrpLnd, SimUstatistics.CrpLnd_H,

SimUstatistics.CrpLnd_L, SimUstatistics.CrpLnd_I, SimUstatistics.CrpLnd_S, SimUstatistics.OthAgri,

SimUstatistics.Grass, SimUstatistics.Forest, SimUstatistics.WetLnd, SimUstatistics.OthNatLnd,

SimUstatistics.NotRel, SimUstatistics.mCrpLnd, SimUstatistics.mCrpLnd_H, SimUstatistics.mCrpLnd_L,

SimUstatistics.mCrpLnd_I, SimUstatistics.mCrpLnd_S, SimUstatistics.mOthAgri, SimUstatistics.mGrass,

SimUstatistics.mForest, SimUstatistics.mWetLnd, SimUstatistics.mOthNatLnd, SimUstatistics.mNotRel

FROM SimUstatistics, D_ColRow30

WHERE (((SimUstatistics.Col_Row30)=[d_colrow30].[col_row30]))

ORDER BY SimUstatistics.SimUID;

3. SQL code of Topo&Soil_MineralSoil(DataPreview&EXPORT) select query:

SELECT ZoneData.Zone_ID, ZoneData.HRU, D_ColRow30.Col30, D_ColRow30.Row30, ZoneData.ZoneLon,

ZoneData.ZoneLat, ZoneData.ZoneAlti, ZoneData.ZoneSlp, SoilAnal_MineralSoil.HG,

SoilAnal_MineralSoil.ALB, SoilAnal_MineralSoil.DEPTH1, SoilAnal_MineralSoil.DEPTH2,

SoilAnal_MineralSoil.DEPTH3, SoilAnal_MineralSoil.DEPTH4, SoilAnal_MineralSoil.DEPTH5,

SoilAnal_MineralSoil.VS1, SoilAnal_MineralSoil.VS2, SoilAnal_MineralSoil.VS3, SoilAnal_MineralSoil.VS4,

SoilAnal_MineralSoil.VS5, SoilAnal_MineralSoil.SAND1, SoilAnal_MineralSoil.SAND2,

SoilAnal_MineralSoil.SAND3, SoilAnal_MineralSoil.SAND4, SoilAnal_MineralSoil.SAND5,

SoilAnal_MineralSoil.SILT1, SoilAnal_MineralSoil.SILT2, SoilAnal_MineralSoil.SILT3,

SoilAnal_MineralSoil.SILT4, SoilAnal_MineralSoil.SILT5, SoilAnal_MineralSoil.CLAY1,

SoilAnal_MineralSoil.CLAY2, SoilAnal_MineralSoil.CLAY3, SoilAnal_MineralSoil.CLAY4,

SoilAnal_MineralSoil.CLAY5, SoilAnal_MineralSoil.BD1, SoilAnal_MineralSoil.BD2,

SoilAnal_MineralSoil.BD3, SoilAnal_MineralSoil.BD4, SoilAnal_MineralSoil.BD5,

SoilAnal_MineralSoil.CEC1, SoilAnal_MineralSoil.CEC2, SoilAnal_MineralSoil.CEC3,

SoilAnal_MineralSoil.CEC4, SoilAnal_MineralSoil.CEC5, SoilAnal_MineralSoil.SB1,

SoilAnal_MineralSoil.SB2, SoilAnal_MineralSoil.SB3, SoilAnal_MineralSoil.SB4, SoilAnal_MineralSoil.SB5,

SoilAnal_MineralSoil.PH1, SoilAnal_MineralSoil.PH2, SoilAnal_MineralSoil.PH3, SoilAnal_MineralSoil.PH4,

SoilAnal_MineralSoil.PH5, SoilAnal_MineralSoil.CARB1, SoilAnal_MineralSoil.CARB2,

SoilAnal_MineralSoil.CARB3, SoilAnal_MineralSoil.CARB4, SoilAnal_MineralSoil.CARB5,

SoilAnal_MineralSoil.CORG1, SoilAnal_MineralSoil.CORG2, SoilAnal_MineralSoil.CORG3,

SoilAnal_MineralSoil.CORG4, SoilAnal_MineralSoil.CORG5, SoilAnal_MineralSoil.FWC1,

SoilAnal_MineralSoil.FWC2, SoilAnal_MineralSoil.FWC3, SoilAnal_MineralSoil.FWC4,

SoilAnal_MineralSoil.FWC5, SoilAnal_MineralSoil.WP1, SoilAnal_MineralSoil.WP2,

SoilAnal_MineralSoil.WP3, SoilAnal_MineralSoil.WP4, SoilAnal_MineralSoil.WP5, SoilAnal_MineralSoil.KS1,

SoilAnal_MineralSoil.KS2, SoilAnal_MineralSoil.KS3, SoilAnal_MineralSoil.KS4, SoilAnal_MineralSoil.KS5,

ClimateReference.Tyndall_X, ClimateReference.Tyndall_Y

FROM ZoneData, SoilAnal_MineralSoil, ClimateReference, D_ColRow30

WHERE (((SoilAnal_MineralSoil.ZoneSTU)=[zonedata].[zonestu]) AND

((ClimateReference.Col_Row30)=[zonedata].[col_row30]) AND

((D_ColRow30.Col_Row30)=[zonedata].[col_row30]))

ORDER BY ZoneData.Zone_ID;

Page 57: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

57

4. SQL code of Topo&Soil_OrganicSoil(DataPreview&EXPORT) select query:

SELECT ZoneData.Zone_ID, ZoneData.HRU, D_ColRow30.Col30, D_ColRow30.Row30, ZoneData.ZoneLon,

ZoneData.ZoneLat, ZoneData.ZoneAlti, ZoneData.ZoneSlp, SoilAnal_OrganicSoil.HG,

SoilAnal_OrganicSoil.ALB, SoilAnal_OrganicSoil.DEPTH1, SoilAnal_OrganicSoil.DEPTH2,

SoilAnal_OrganicSoil.DEPTH3, SoilAnal_OrganicSoil.DEPTH4, SoilAnal_OrganicSoil.DEPTH5,

SoilAnal_OrganicSoil.VS1, SoilAnal_OrganicSoil.VS2, SoilAnal_OrganicSoil.VS3, SoilAnal_OrganicSoil.VS4,

SoilAnal_OrganicSoil.VS5, SoilAnal_OrganicSoil.SAND1, SoilAnal_OrganicSoil.SAND2,

SoilAnal_OrganicSoil.SAND3, SoilAnal_OrganicSoil.SAND4, SoilAnal_OrganicSoil.SAND5,

SoilAnal_OrganicSoil.SILT1, SoilAnal_OrganicSoil.SILT2, SoilAnal_OrganicSoil.SILT3,

SoilAnal_OrganicSoil.SILT4, SoilAnal_OrganicSoil.SILT5, SoilAnal_OrganicSoil.CLAY1,

SoilAnal_OrganicSoil.CLAY2, SoilAnal_OrganicSoil.CLAY3, SoilAnal_OrganicSoil.CLAY4,

SoilAnal_OrganicSoil.CLAY5, SoilAnal_OrganicSoil.BD1, SoilAnal_OrganicSoil.BD2,

SoilAnal_OrganicSoil.BD3, SoilAnal_OrganicSoil.BD4, SoilAnal_OrganicSoil.BD5,

SoilAnal_OrganicSoil.CEC1, SoilAnal_OrganicSoil.CEC2, SoilAnal_OrganicSoil.CEC3,

SoilAnal_OrganicSoil.CEC4, SoilAnal_OrganicSoil.CEC5, SoilAnal_OrganicSoil.SB1,

SoilAnal_OrganicSoil.SB2, SoilAnal_OrganicSoil.SB3, SoilAnal_OrganicSoil.SB4, SoilAnal_OrganicSoil.SB5,

SoilAnal_OrganicSoil.PH1, SoilAnal_OrganicSoil.PH2, SoilAnal_OrganicSoil.PH3,

SoilAnal_OrganicSoil.PH4, SoilAnal_OrganicSoil.PH5, SoilAnal_OrganicSoil.CARB1,

SoilAnal_OrganicSoil.CARB2, SoilAnal_OrganicSoil.CARB3, SoilAnal_OrganicSoil.CARB4,

SoilAnal_OrganicSoil.CARB5, SoilAnal_OrganicSoil.CORG1, SoilAnal_OrganicSoil.CORG2,

SoilAnal_OrganicSoil.CORG3, SoilAnal_OrganicSoil.CORG4, SoilAnal_OrganicSoil.CORG5,

SoilAnal_OrganicSoil.FWC1, SoilAnal_OrganicSoil.FWC2, SoilAnal_OrganicSoil.FWC3,

SoilAnal_OrganicSoil.FWC4, SoilAnal_OrganicSoil.FWC5, SoilAnal_OrganicSoil.WP1,

SoilAnal_OrganicSoil.WP2, SoilAnal_OrganicSoil.WP3, SoilAnal_OrganicSoil.WP4,

SoilAnal_OrganicSoil.WP5, SoilAnal_OrganicSoil.KS1, SoilAnal_OrganicSoil.KS2, SoilAnal_OrganicSoil.KS3,

SoilAnal_OrganicSoil.KS4, SoilAnal_OrganicSoil.KS5, ClimateReference.Tyndall_X,

ClimateReference.Tyndall_Y

FROM ZoneData, SoilAnal_OrganicSoil, ClimateReference, D_ColRow30

WHERE (((SoilAnal_OrganicSoil.ZoneSTU)=[zonedata].[zonestu]) AND

((ClimateReference.Col_Row30)=[zonedata].[col_row30]) AND

((D_ColRow30.Col_Row30)=[ZoneData].[Col_Row30]))

ORDER BY ZoneData.Zone_ID;

5. SQL code of CropCalendar(DataPreview&EXPORT) select query:

SELECT CropCalendar.COUNTRY, CropCalendar.CROP, CropCalendar.CrpCalSpec, CropCalendar.OPER,

CropCalendar.YEA, CropCalendar.DAY, CropCalendar.DAT, CropCalendar.mHI, CropCalendar.mIR,

CropCalendar.mLI, CropCalendar.mSS

FROM CropCalendar

ORDER BY CropCalendar.COUNTRY, CropCalendar.CROP, CropCalendar.CrpCalSpec, CropCalendar.DAT;

6. SQL code of CropShare(DataPreview&EXPORT) select query:

SELECT CropShare.SimUID, D_ColRow30.Col30, D_ColRow30.Row30, CropShare.CrpCalSpec,

CropShare.CROP, CropShare.CultType, CropShare.RotType, CropShare.CrpArea, CropShare.SimUmask,

CropShare.absSimUmask, CropShare.CropMask, CropShare.AltMask, CropShare.MngSystem

FROM CropShare, D_ColRow30

WHERE (((CropShare.MngSystem)="HI") AND ((CropShare.Col_Row30)=[d_colrow30].[col_row30]))

ORDER BY CropShare.SimUID;

7. SQL code of ManureApplication(DataPreview&EXPORT) select query:

SELECT ManureApplication.SimUID, ManureApplication.HRU, D_ColRow30.Col30, D_ColRow30.Row30,

ManureApplication.COUNTRY, ManureApplication.Nrate, ManureApplication.Manure

FROM ManureApplication, D_ColRow30

Page 58: Global Earth Observation Benefit Assessment: Now, Next, and … · 2009-03-13 · Global Earth Observation – Benefit Assessment: Now, Next, and Emerging GEO-BENE global database

58

WHERE (((D_ColRow30.Col_Row30)=[manureapplication].[col_row30]))

ORDER BY ManureApplication.HRU;